ACER project members; Sanchez Goñi, Maria Fernanda; Desprat, Stéphanie; Daniau, Anne-Laure; Allen, Judy R M; Anderson, R Scott; Behling, Hermann; Bonnefille, Raymonde; Cheddadi, Rachid; Combourieu-Nebout, Nathalie; Dupont, Lydie M; Fletcher, William J; González, Catalina; Grigg, Laurie D; Grimm, Eric C; Hayashi, Ryoma; Helmens, Karin F; Hessler, Ines; Heusser, Linda E; Hooghiemstra, Henry; Huntley, Brian; Igarashi, Yaeko; Irino, Tomohisa; Jacobs, Bonnie Fine; Jiménez-Moreno, Gonzalo; Kawai, Sayuri; Kumon, Fujio; Lawson, Ian T; Lebamba, Judicael; Ledru, Marie-Pierre; Lézine, Anne-Marie; Liew, Ping-Mei; Londeix, Laurent; López-Martinez, Constancia; Magri, Donatella; Maley, Jean; Margari, Vasiliki; Marret, Fabienne; Müller, Ulrich C; Naughton, Filipa; Novenko, Elena Y; Oba, Tadamichi; Roucoux, Katherine H; Takahara, Hikaru; Tzedakis, Polychronis C; Vincens, Annie; Whitlock, Cathy L; Willard, Debra A; Yamamoto, Masanobu (2017): CLAM age model and biomes of sediment core Little_Lake [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.872805
Always quote citation above when using data! You can download the citation in several formats below.
Related to:
Fletcher, William J; Sanchez Goñi, Maria Fernanda; Allen, Judy R M; Cheddadi, Rachid; Combourieu-Nebout, Nathalie; Huntley, Brian; Lawson, Ian T; Londeix, Laurent; Magri, Donatella; Margari, Vasiliki; Müller, Ulrich C; Naughton, Filipa; Novenko, Elena Y; Roucoux, Katherine H; Tzedakis, Polychronis C (2010): Millennial-scale variability during the last glacial in vegetation records from Europe. Quaternary Science Reviews, 29(21-22), 2839-2864, https://doi.org/10.1016/j.quascirev.2009.11.015
Hessler, Ines; Dupont, Lydie M; Bonnefille, Raymonde; Behling, Hermann; González, Catalina; Helmens, Karin F; Hooghiemstra, Henry; Lebamba, Judicael; Ledru, Marie-Pierre; Lézine, Anne-Marie; Maley, Jean; Marret, Fabienne; Vincens, Annie (2010): Millennial-scale changes in vegetation records from tropical Africa and South America during the last glacial. Quaternary Science Reviews, 29(21-22), 2882-2899, https://doi.org/10.1016/j.quascirev.2009.11.029
Jiménez-Moreno, Gonzalo; Anderson, R Scott; Desprat, Stéphanie; Grigg, Laurie D; Grimm, Eric C; Heusser, Linda E; Jacobs, Bonnie Fine; López-Martinez, Constancia; Whitlock, Cathy L; Willard, Debra A (2010): Millennial-scale variability during the last glacial in vegetation records from North America. Quaternary Science Reviews, 29(21-22), 2865-2881, https://doi.org/10.1016/j.quascirev.2009.12.013
Sanchez Goñi, Maria Fernanda; Desprat, Stéphanie; Daniau, Anne-Laure; Bassinot, Franck C; Polanco-Martínez, Josué M; Harrison, Sandy P; Allen, Judy R M; Anderson, R Scott; Behling, Hermann; Bonnefille, Raymonde; Burjachs, Francesc; Carrión, José S; Cheddadi, Rachid; Clark, James S; Combourieu-Nebout, Nathalie; Courtney-Mustaphi, Colin J; DeBusk, Georg H; Dupont, Lydie M; Finch, Jemma M; Fletcher, William J; Giardini, Marco; González, Catalina; Gosling, William D; Grigg, Laurie D; Grimm, Eric C; Hayashi, Ryoma; Helmens, Karin F; Heusser, Linda E; Hill, Trevor R; Hope, Geoffrey; Huntley, Brian; Igarashi, Yaeko; Irino, Tomohisa; Jacobs, Bonnie Fine; Jiménez-Moreno, Gonzalo; Kawai, Sayuri; Kershaw, A Peter; Kumon, Fujio; Lawson, Ian T; Ledru, Marie-Pierre; Lézine, Anne-Marie; Liew, Ping-Mei; Magri, Donatella; Marchant, Robert; Margari, Vasiliki; Mayle, Francis E; McKenzie, G Merna; Moss, Patrick T; Müller, Stefanie; Müller, Ulrich C; Naughton, Filipa; Newnham, Rewi M; Oba, Tadamichi; Pérez-Obiol, Ramon P; Pini, Roberta; Ravazzi, Cesare; Roucoux, Katherine H; Rucina, Stephen M; Scott, Louis; Takahara, Hikaru; Tzedakis, Polychronis C; Urrego, Dunia H; van Geel, Bas; Valencia, Bryan G; Vandergoes, Marcus J; Vincens, Annie; Whitlock, Cathy L; Willard, Debra A; Yamamoto, Masanobu (2017): The ACER pollen and charcoal database: a global resource to document vegetation and fire response to abrupt climate changes during the last glacial period. Earth System Science Data, 9(2), 679-695, https://doi.org/10.5194/essd-9-679-2017
Takahara, Hikaru; Igarashi, Yaeko; Hayashi, Ryoma; Kumon, Fujio; Liew, Ping-Mei; Yamamoto, Masanobu; Kawai, Sayuri; Oba, Tadamichi; Irino, Tomohisa (2010): Millennial-scale variability in vegetation records from the East Asian Islands: Taiwan, Japan and Sakhalin. Quaternary Science Reviews, 29(21-22), 2900-2917, https://doi.org/10.1016/j.quascirev.2009.11.026
Project(s):
Coverage:
Latitude: 44.160000 * Longitude: -123.580000
Minimum DEPTH, sediment/rock: 10.560 m * Maximum DEPTH, sediment/rock: 17.220 m
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | DEPTH, sediment/rock | Depth sed | m | Geocode | ||
| 2 | AGE | Age | ka BP | Sanchez Goñi, Maria Fernanda | Geocode – original age | |
| 3 | Calendar age, minimum/young | Cal age min | ka BP | Sanchez Goñi, Maria Fernanda | Classical age-modeling approach, CLAM (Blaauw, 2010) | CLAM_min95 |
| 4 | Calendar age, maximum/old | Cal age max | ka BP | Sanchez Goñi, Maria Fernanda | Classical age-modeling approach, CLAM (Blaauw, 2010) | CLAM_max95 |
| 5 | Calendar age | Cal age | ka BP | Sanchez Goñi, Maria Fernanda | Classical age-modeling approach, CLAM (Blaauw, 2010) | CLAM_best |
| 6 | Accumulation model | Accu model | a/cm | Sanchez Goñi, Maria Fernanda | Classical age-modeling approach, CLAM (Blaauw, 2010) | |
| 7 | Type of age model | Age model type | Sanchez Goñi, Maria Fernanda | |||
| 8 | Sample ID | Sample ID | Sanchez Goñi, Maria Fernanda | ACER sample ID | ||
| 9 | Pollen, boreal forest | Pollen bo forest | % | Sanchez Goñi, Maria Fernanda | ||
| 10 | Pollen, temperate forest | Pollen te forest | % | Sanchez Goñi, Maria Fernanda |
License:
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC-BY-NC-ND-3.0)
Size:
976 data points
Data
| 1 Depth sed [m] | 2 Age [ka BP] | 3 Cal age min [ka BP] | 4 Cal age max [ka BP] | 5 Cal age [ka BP] | 6 Accu model [a/cm] | 7 Age model type | 8 Sample ID | 9 Pollen bo forest [%] | 10 Pollen te forest [%] |
|---|---|---|---|---|---|---|---|---|---|
| 10.560 | 14.14400 | 12.01750 | 12.86820 | 12.49680 | 22.401 | Polynomial regression-order 3 | 4275 | 42.576 | 15.721 |
| 10.920 | 14.86400 | 12.86640 | 13.72690 | 13.33450 | 24.196 | Polynomial regression-order 3 | 4276 | 52.160 | 11.265 |
| 11.300 | 15.72700 | 13.84430 | 14.68810 | 14.28980 | 26.140 | Polynomial regression-order 3 | 4277 | 56.716 | 8.706 |
| 11.380 | 15.92200 | 14.05770 | 14.89970 | 14.50030 | 26.556 | Polynomial regression-order 3 | 4278 | 49.215 | 12.565 |
| 11.480 | 16.17200 | 14.32850 | 15.16670 | 14.76830 | 27.079 | Polynomial regression-order 3 | 4279 | 62.657 | 10.526 |
| 11.520 | 16.27400 | 14.43860 | 15.27590 | 14.87690 | 27.289 | Polynomial regression-order 3 | 4280 | 71.396 | 7.432 |
| 11.560 | 16.37700 | 14.55160 | 15.38570 | 14.98640 | 27.499 | Polynomial regression-order 3 | 4281 | 64.789 | 7.277 |
| 11.600 | 16.48200 | 14.66650 | 15.49530 | 15.09670 | 27.711 | Polynomial regression-order 3 | 4282 | 80.508 | 4.802 |
| 11.640 | 16.58700 | 14.77990 | 15.60690 | 15.20780 | 27.922 | Polynomial regression-order 3 | 4283 | 60.976 | 3.752 |
| 11.680 | 16.69400 | 14.89350 | 15.72020 | 15.31980 | 28.135 | Polynomial regression-order 3 | 4284 | 63.033 | 3.318 |
| 11.720 | 16.80200 | 15.00730 | 15.83520 | 15.43270 | 28.348 | Polynomial regression-order 3 | 4285 | 68.000 | 2.947 |
| 11.760 | 16.91100 | 15.12080 | 15.94990 | 15.54640 | 28.561 | Polynomial regression-order 3 | 4286 | 63.753 | 1.542 |
| 11.800 | 17.02100 | 15.23600 | 16.06500 | 15.66100 | 28.775 | Polynomial regression-order 3 | 4287 | 62.523 | 3.896 |
| 11.840 | 17.13300 | 15.35480 | 16.18160 | 15.77640 | 28.990 | Polynomial regression-order 3 | 4288 | 76.633 | 0.754 |
| 11.880 | 17.24500 | 15.46810 | 16.29890 | 15.89270 | 29.205 | Polynomial regression-order 3 | 4289 | 70.577 | 1.988 |
| 11.920 | 17.35900 | 15.58630 | 16.41690 | 16.00980 | 29.420 | Polynomial regression-order 3 | 4290 | 69.930 | 4.662 |
| 12.000 | 17.59000 | 15.82560 | 16.65580 | 16.24670 | 29.853 | Polynomial regression-order 3 | 4291 | 56.962 | 4.430 |
| 12.060 | 17.76600 | 16.00460 | 16.83480 | 16.42660 | 30.180 | Polynomial regression-order 3 | 4292 | 66.167 | 4.000 |
| 12.120 | 17.94500 | 16.18090 | 17.02270 | 16.60850 | 30.507 | Polynomial regression-order 3 | 4293 | 42.790 | 3.310 |
| 12.180 | 18.12600 | 16.35850 | 17.21090 | 16.79240 | 30.836 | Polynomial regression-order 3 | 4294 | 65.553 | 3.758 |
| 12.240 | 18.31000 | 16.54170 | 17.40010 | 16.97820 | 31.166 | Polynomial regression-order 3 | 4295 | 52.492 | 6.977 |
| 12.300 | 18.49700 | 16.72490 | 17.59380 | 17.16610 | 31.497 | Polynomial regression-order 3 | 4296 | 69.583 | 3.333 |
| 12.360 | 18.68600 | 16.90640 | 17.78940 | 17.35590 | 31.830 | Polynomial regression-order 3 | 4297 | 79.328 | 2.196 |
| 12.420 | 18.87800 | 17.08800 | 17.98890 | 17.54770 | 32.164 | Polynomial regression-order 3 | 4298 | 57.958 | 3.003 |
| 12.540 | 19.26900 | 17.46390 | 18.39120 | 17.93730 | 32.835 | Polynomial regression-order 3 | 4299 | 60.328 | 3.934 |
| 12.580 | 19.40200 | 17.59040 | 18.52750 | 18.06900 | 33.060 | Polynomial regression-order 3 | 4300 | 71.108 | 4.909 |
| 12.620 | 19.53600 | 17.71840 | 18.66540 | 18.20160 | 33.285 | Polynomial regression-order 3 | 4301 | 70.711 | 2.426 |
| 12.660 | 19.67100 | 17.84600 | 18.80180 | 18.33510 | 33.511 | Polynomial regression-order 3 | 4302 | 62.980 | 3.160 |
| 12.740 | 19.94500 | 18.10430 | 19.08650 | 18.60470 | 33.965 | Polynomial regression-order 3 | 4303 | 71.014 | 2.319 |
| 12.820 | 20.22400 | 18.36790 | 19.37630 | 18.87810 | 34.421 | Polynomial regression-order 3 | 4304 | 61.770 | 4.520 |
| 12.860 | 20.36400 | 18.49690 | 19.52410 | 19.01610 | 34.650 | Polynomial regression-order 3 | 4305 | 57.317 | 7.317 |
| 12.900 | 20.50700 | 18.62710 | 19.67090 | 19.15500 | 34.879 | Polynomial regression-order 3 | 4306 | 63.062 | 6.156 |
| 12.980 | 20.79400 | 18.88820 | 19.96780 | 19.43570 | 35.340 | Polynomial regression-order 3 | 4307 | 62.295 | 5.961 |
| 13.040 | 21.01300 | 19.08880 | 20.19520 | 19.64860 | 35.687 | Polynomial regression-order 3 | 4308 | 76.724 | 5.747 |
| 13.080 | 21.16000 | 19.22390 | 20.34580 | 19.79170 | 35.918 | Polynomial regression-order 3 | 4309 | 65.909 | 5.682 |
| 13.120 | 21.30900 | 19.35970 | 20.49960 | 19.93570 | 36.151 | Polynomial regression-order 3 | 4310 | 75.452 | 6.718 |
| 13.160 | 21.45800 | 19.49450 | 20.65310 | 20.08070 | 36.384 | Polynomial regression-order 3 | 4311 | 77.843 | 4.314 |
| 13.200 | 21.60900 | 19.63060 | 20.80700 | 20.22650 | 36.618 | Polynomial regression-order 3 | 4312 | 57.616 | 2.980 |
| 13.240 | 21.76100 | 19.76940 | 20.96050 | 20.37340 | 36.852 | Polynomial regression-order 3 | 4313 | 57.635 | 10.591 |
| 13.280 | 21.91400 | 19.90860 | 21.12030 | 20.52110 | 37.086 | Polynomial regression-order 3 | 4314 | 74.462 | 7.538 |
| 13.320 | 22.06900 | 20.04930 | 21.27750 | 20.66980 | 37.322 | Polynomial regression-order 3 | 4315 | 77.303 | 5.263 |
| 13.360 | 22.22400 | 20.18960 | 21.43920 | 20.81950 | 37.558 | Polynomial regression-order 3 | 4316 | 81.159 | 3.080 |
| 13.400 | 22.38100 | 20.33200 | 21.59710 | 20.97000 | 37.794 | Polynomial regression-order 3 | 4317 | 75.410 | 4.098 |
| 13.440 | 22.53800 | 20.47430 | 21.75640 | 21.12160 | 38.031 | Polynomial regression-order 3 | 4318 | 66.109 | 3.138 |
| 13.500 | 22.77700 | 20.68890 | 21.99830 | 21.35060 | 38.387 | Polynomial regression-order 3 | 4319 | 77.159 | 5.571 |
| 13.540 | 22.93800 | 20.83770 | 22.16070 | 21.50460 | 38.626 | Polynomial regression-order 3 | 4320 | 69.467 | 2.664 |
| 13.620 | 23.26300 | 21.13270 | 22.48970 | 21.81520 | 39.104 | Polynomial regression-order 3 | 4322 | 76.124 | 1.966 |
| 13.700 | 23.59200 | 21.43110 | 22.81920 | 22.12970 | 39.585 | Polynomial regression-order 3 | 4324 | 78.338 | 2.771 |
| 13.780 | 23.92600 | 21.73460 | 23.15410 | 22.44810 | 40.067 | Polynomial regression-order 3 | 4325 | 64.987 | 4.244 |
| 13.860 | 24.26500 | 22.04080 | 23.49370 | 22.77030 | 40.553 | Polynomial regression-order 3 | 4327 | 65.546 | 7.283 |
| 13.940 | 24.60800 | 22.35000 | 23.83570 | 23.09650 | 41.040 | Polynomial regression-order 3 | 4329 | 55.125 | 11.357 |
| 14.000 | 24.86900 | 22.58700 | 24.09550 | 23.34360 | 41.407 | Polynomial regression-order 3 | 4330 | 55.952 | 5.655 |
| 14.060 | 25.13200 | 22.82510 | 24.35870 | 23.59300 | 41.775 | Polynomial regression-order 3 | 4332 | 66.667 | 13.248 |
| 14.120 | 25.39800 | 23.06510 | 24.62130 | 23.84460 | 42.145 | Polynomial regression-order 3 | 4334 | 78.006 | 7.771 |
| 14.180 | 25.66600 | 23.30840 | 24.88900 | 24.09840 | 42.515 | Polynomial regression-order 3 | 4336 | 77.895 | 5.614 |
| 14.240 | 25.93700 | 23.55440 | 25.15690 | 24.35440 | 42.887 | Polynomial regression-order 3 | 4338 | 85.286 | 2.997 |
| 14.280 | 26.11900 | 23.71850 | 25.33890 | 24.52630 | 43.136 | Polynomial regression-order 3 | 4339 | 81.682 | 4.112 |
| 14.320 | 26.30200 | 23.88560 | 25.51870 | 24.69920 | 43.385 | Polynomial regression-order 3 | 4340 | 78.734 | 7.342 |
| 14.360 | 26.48700 | 24.05240 | 25.69790 | 24.87310 | 43.635 | Polynomial regression-order 3 | 4341 | 67.841 | 9.692 |
| 14.400 | 26.67200 | 24.21980 | 25.88110 | 25.04810 | 43.885 | Polynomial regression-order 3 | 4342 | 64.856 | 12.141 |
| 14.440 | 26.85900 | 24.39110 | 26.06550 | 25.22400 | 44.136 | Polynomial regression-order 3 | 4343 | 45.573 | 14.844 |
| 14.480 | 27.04700 | 24.56240 | 26.25040 | 25.40090 | 44.388 | Polynomial regression-order 3 | 4344 | 83.254 | 5.502 |
| 14.520 | 27.23600 | 24.73520 | 26.43550 | 25.57880 | 44.640 | Polynomial regression-order 3 | 4345 | 77.994 | 10.306 |
| 14.560 | 27.42600 | 24.91020 | 26.62030 | 25.75780 | 44.893 | Polynomial regression-order 3 | 4346 | 76.571 | 9.429 |
| 14.600 | 27.61700 | 25.08920 | 26.80930 | 25.93770 | 45.146 | Polynomial regression-order 3 | 4347 | 69.895 | 8.000 |
| 14.640 | 27.81000 | 25.26660 | 26.99540 | 26.11870 | 45.399 | Polynomial regression-order 3 | 4348 | 36.381 | 44.190 |
| 14.680 | 28.00400 | 25.44490 | 27.18390 | 26.30070 | 45.654 | Polynomial regression-order 3 | 4349 | 47.765 | 28.492 |
| 14.740 | 28.29600 | 25.71500 | 27.46860 | 26.57550 | 46.036 | Polynomial regression-order 3 | 4350 | 60.857 | 13.429 |
| 14.800 | 28.59200 | 25.98900 | 27.75530 | 26.85270 | 46.420 | Polynomial regression-order 3 | 4351 | 55.214 | 12.650 |
| 14.860 | 28.89000 | 26.26600 | 28.04200 | 27.13220 | 46.805 | Polynomial regression-order 3 | 4352 | 66.221 | 14.381 |
| 14.920 | 29.19000 | 26.54130 | 28.32980 | 27.41400 | 47.191 | Polynomial regression-order 3 | 4353 | 48.517 | 19.023 |
| 15.000 | 29.59500 | 26.91840 | 28.71170 | 27.79330 | 47.708 | Polynomial regression-order 3 | 4354 | 64.978 | 14.903 |
| 15.060 | 29.90200 | 27.20560 | 29.00430 | 28.08050 | 48.098 | Polynomial regression-order 3 | 4355 | 54.439 | 18.224 |
| 15.120 | 30.21100 | 27.49600 | 29.29810 | 28.37010 | 48.488 | Polynomial regression-order 3 | 4356 | 48.764 | 30.972 |
| 15.180 | 30.52300 | 27.78730 | 29.59290 | 28.66200 | 48.880 | Polynomial regression-order 3 | 4357 | 59.277 | 20.241 |
| 15.220 | 30.73200 | 27.98490 | 29.79160 | 28.85790 | 49.142 | Polynomial regression-order 3 | 4358 | 70.163 | 14.141 |
| 15.280 | 31.04800 | 28.28250 | 30.08930 | 29.15380 | 49.535 | Polynomial regression-order 3 | 4359 | 65.578 | 13.769 |
| 15.340 | 31.36700 | 28.58140 | 30.38740 | 29.45200 | 49.931 | Polynomial regression-order 3 | 4360 | 82.549 | 4.804 |
| 15.400 | 31.68800 | 28.88820 | 30.68730 | 29.75250 | 50.327 | Polynomial regression-order 3 | 4361 | 57.793 | 19.089 |
| 15.480 | 32.12100 | 29.29710 | 31.08910 | 30.15700 | 50.857 | Polynomial regression-order 3 | 4362 | 65.247 | 15.765 |
| 15.540 | 32.44800 | 29.60560 | 31.39600 | 30.46310 | 51.257 | Polynomial regression-order 3 | 4363 | 34.309 | 37.500 |
| 15.580 | 32.66800 | 29.80870 | 31.59900 | 30.66860 | 51.523 | Polynomial regression-order 3 | 4364 | 49.322 | 36.585 |
| 15.620 | 32.88900 | 30.02030 | 31.80380 | 30.87510 | 51.791 | Polynomial regression-order 3 | 4365 | 52.927 | 29.742 |
| 15.660 | 33.11100 | 30.23140 | 32.00970 | 31.08260 | 52.059 | Polynomial regression-order 3 | 4366 | 39.958 | 47.490 |
| 15.700 | 33.33400 | 30.44340 | 32.21630 | 31.29130 | 52.327 | Polynomial regression-order 3 | 4367 | 67.027 | 21.982 |
| 15.740 | 33.55900 | 30.65660 | 32.42470 | 31.50100 | 52.597 | Polynomial regression-order 3 | 4368 | 59.432 | 22.110 |
| 15.780 | 33.78400 | 30.87490 | 32.63360 | 31.71180 | 52.866 | Polynomial regression-order 3 | 4369 | 46.209 | 26.777 |
| 15.820 | 34.01100 | 31.08670 | 32.84460 | 31.92360 | 53.137 | Polynomial regression-order 3 | 4370 | 57.284 | 17.284 |
| 15.860 | 34.23900 | 31.30720 | 33.05180 | 32.13660 | 53.407 | Polynomial regression-order 3 | 4371 | 57.034 | 18.441 |
| 15.900 | 34.46800 | 31.52360 | 33.26220 | 32.35060 | 53.679 | Polynomial regression-order 3 | 4372 | 66.359 | 13.863 |
| 15.980 | 34.93000 | 31.96870 | 33.68910 | 32.78200 | 54.223 | Polynomial regression-order 3 | 4373 | 52.381 | 19.608 |
| 16.020 | 35.16200 | 32.18840 | 33.90280 | 32.99930 | 54.496 | Polynomial regression-order 3 | 4374 | 52.312 | 19.653 |
| 16.060 | 35.39600 | 32.41130 | 34.11650 | 33.21770 | 54.770 | Polynomial regression-order 3 | 4375 | 47.396 | 33.594 |
| 16.100 | 35.63100 | 32.63360 | 34.33530 | 33.43720 | 55.044 | Polynomial regression-order 3 | 4376 | 72.021 | 14.680 |
| 16.140 | 35.86700 | 32.85850 | 34.55290 | 33.65770 | 55.319 | Polynomial regression-order 3 | 4377 | 36.243 | 43.651 |
| 16.180 | 36.10400 | 33.08560 | 34.76780 | 33.87940 | 55.594 | Polynomial regression-order 3 | 4378 | 47.453 | 36.681 |
| 16.220 | 36.34200 | 33.31430 | 34.98560 | 34.10220 | 55.870 | Polynomial regression-order 3 | 4379 | 52.052 | 32.276 |
| 16.260 | 36.58200 | 33.54590 | 35.20370 | 34.32610 | 56.146 | Polynomial regression-order 3 | 4380 | 58.036 | 16.295 |
| 16.300 | 36.82200 | 33.77620 | 35.42140 | 34.55110 | 56.423 | Polynomial regression-order 3 | 4381 | 45.122 | 20.732 |
| 16.340 | 37.06400 | 34.00870 | 35.64420 | 34.77720 | 56.701 | Polynomial regression-order 3 | 4382 | 32.079 | 46.535 |
| 16.380 | 37.30700 | 34.23900 | 35.86930 | 35.00440 | 56.979 | Polynomial regression-order 3 | 4383 | 41.089 | 40.842 |
| 16.420 | 37.55100 | 34.46620 | 36.09580 | 35.23280 | 57.257 | Polynomial regression-order 3 | 4384 | 77.458 | 11.186 |
| 16.460 | 37.79700 | 34.69150 | 36.32170 | 35.46220 | 57.537 | Polynomial regression-order 3 | 4385 | 67.502 | 16.972 |
| 16.500 | 38.04300 | 34.92200 | 36.54830 | 35.69280 | 57.816 | Polynomial regression-order 3 | 4386 | 30.939 | 32.320 |
| 16.540 | 38.29100 | 35.14980 | 36.78270 | 35.92450 | 58.097 | Polynomial regression-order 3 | 4387 | 46.317 | 33.708 |
| 16.580 | 38.53900 | 35.38110 | 37.01180 | 36.15730 | 58.378 | Polynomial regression-order 3 | 4388 | 41.736 | 34.091 |
| 16.620 | 38.78900 | 35.62200 | 37.23960 | 36.39120 | 58.659 | Polynomial regression-order 3 | 4389 | 55.882 | 23.775 |
| 16.660 | 39.04000 | 35.85130 | 37.47300 | 36.62630 | 58.941 | Polynomial regression-order 3 | 4390 | 51.671 | 24.807 |
| 16.700 | 39.29300 | 36.09210 | 37.71140 | 36.86250 | 59.224 | Polynomial regression-order 3 | 4391 | 69.655 | 19.828 |
| 16.740 | 39.54600 | 36.32930 | 37.94770 | 37.09980 | 59.507 | Polynomial regression-order 3 | 4392 | 25.806 | 58.669 |
| 16.780 | 39.80100 | 36.56540 | 38.19070 | 37.33820 | 59.790 | Polynomial regression-order 3 | 4393 | 33.807 | 53.397 |
| 16.820 | 40.05600 | 36.80030 | 38.43170 | 37.57780 | 60.075 | Polynomial regression-order 3 | 4394 | 29.006 | 57.853 |
| 16.860 | 40.31300 | 37.03500 | 38.67270 | 37.81850 | 60.360 | Polynomial regression-order 3 | 4395 | 44.355 | 41.129 |
| 16.900 | 40.57100 | 37.26630 | 38.91380 | 38.06040 | 60.645 | Polynomial regression-order 3 | 4396 | 55.666 | 30.878 |
| 16.940 | 40.83000 | 37.50040 | 39.16080 | 38.30340 | 60.931 | Polynomial regression-order 3 | 4397 | 49.401 | 27.545 |
| 16.980 | 41.09100 | 37.72970 | 39.40640 | 38.54760 | 61.217 | Polynomial regression-order 3 | 4398 | 65.415 | 20.949 |
| 17.030 | 41.41800 | 38.01730 | 39.72300 | 38.85440 | 61.576 | Polynomial regression-order 3 | 4399 | 59.716 | 27.962 |
| 17.060 | 41.61500 | 38.18770 | 39.90830 | 39.03930 | 61.792 | Polynomial regression-order 3 | 4400 | 56.612 | 32.094 |
| 17.100 | 41.87900 | 38.42430 | 40.15930 | 39.28690 | 62.080 | Polynomial regression-order 3 | 4401 | 66.335 | 19.323 |
| 17.140 | 42.14400 | 38.65540 | 40.41570 | 39.53570 | 62.369 | Polynomial regression-order 3 | 4402 | 69.659 | 22.338 |
| 17.180 | 42.41000 | 38.89100 | 40.67760 | 39.78560 | 62.658 | Polynomial regression-order 3 | 4403 | 20.882 | 69.706 |
| 17.220 | 42.67700 | 39.12740 | 40.93840 | 40.03660 | 62.948 | Polynomial regression-order 3 | 4404 | 30.268 | 60.153 |