Supplementary material to "CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 1: Model framework and site-level performance"
Bibliographic record
Abstract
1 Outputs produced by the CLASSIC benchmark framework Table S1.Statistics of CLASSIC simulated RNS against FLUXNET observations.RMSE (in W m -2 )and R 2 are calculated as described in Section 5.1.site biome RMSE R 2 AU-Tum EBF 37.463 0.706 BR-Sa1 EBF 19.139 0.146 FR-Pue EBF 11.356 0.968 GF-Guy EBF 13.576 0.594 GH-Ank EBF 24.739 -0.127 MY-PSO EBF 15.12 -0.211 CA-Qfo ENF 8.39 0.975 CZ-BK1 ENF 17.917 0.885 DE-Tha ENF 10.329 0.968 FI-Hyy ENF 8.895 0.974 IT-Lav ENF 12.625 0.966 IT-SRo ENF 32.33 0.826 NL-Loo ENF 6.098 0.99 RU-Fyo ENF 15.836 0.931 CA-TPD DBF 11.77 0.962 DK-Sor DBF 7.238 0.984 FR-Fon DBF 19.794 0.881 US-WCr DBF 14.094 0.921 ZM-Mon DBF 35.981 -1.582 CG-Tch SAV 20.521 0.094 SD-Dem SAV 15.202 0.552 ZA-Kru SAV 32.45 0.306 CN-Dan GRA 17.595 0.871 IT-Tor GRA 25.827 0.857 PA-SPs GRA 15.344 0.444 RU-Ha1 GRA 21.409 0.834 US-Wkg GRA 12.407 0.911 DE-Kli CRO 9.195 0.971 ES-LgS OSH 41.291 0.588 RU-Che WET 26.225 0.749 RU-SkP DNF 34.616 0.67 Table S2.Statistics of CLASSIC simulated HFLS against FLUXNET observations.RMSE (in W m -2 ) and R 2 are calculated as described in Section 5.1.site biome RMSE R 2 AU-Tum EBF 38.84 -0.155 FR-Pue EBF 17.264 0.553 GH-Ank EBF 33.97 -0.27 MY-PSO EBF 33.972 -21.619CA-Qfo ENF 21.19 0.077 DE-Tha ENF 29.209 -0.163 FI-Hyy ENF 24.056 0.193 IT-Lav ENF 21.906 0.127 IT-SRo ENF 42.414 -0.443 NL-Loo ENF 36.341-0.801 RU-Fyo ENF 22.074 0.549 CA-TPD DBF 30.234 0.454 DK-Sor DBF 34.76 0.299 US-WCr DBF 23.801 0.55 ZM-Mon DBF 47.782 -0.605 DE-Kli CRO 27.613 0.317 ES-LgS OSH 16.385 0.046 IT-Tor GRA 73.755 -0.076 RU-Ha1 GRA 37.294 -0.772 US-Wkg GRA 17.586 0.423 RU-SkP DNF 24.642 0.314 SD-Dem SAV 43.059 -0.868 ZA-Kru SAV 40.427 -0.24 Table S3.Statistics of CLASSIC simulated HFSS against FLUXNET observations.RMSE (in W m -2 ) and R 2 are calculated as described in Section 5.1.site biome RMSE R 2 AU-Tum EBF 17.851 0.511 FR-Pue EBF 18.42 0.855 GH-Ank EBF 24.128 -1.163 MY-PSO EBF 16.648 -1.69 CA-Qfo ENF 15.236 0.826 DE-Tha ENF 19.401 0.61 FI-Hyy ENF 19.359 0.634 IT-Lav ENF 16.458 0.84 IT-SRo ENF 32.831 0.749 NL-Loo ENF 37.758 0.252 RU-Fyo ENF 15.457 0.798 CA-TPD DBF 28.451 0.359 DK-Sor DBF 35.824 -0.468 US-WCr DBF 35.281 -1.103 ZM-Mon DBF 27.048 -0.585 DE-Kli CRO 21.056 0.355 ES-LgS OSH 56.334 -0.225 IT-Tor GRA 34.887 0.158 RU-Ha1 GRA 21.299 0.094 US-Wkg GRA 11.138 0.849 RU-SkP DNF 26.826 0.508 SD-Dem SAV 21.345 -1.731 ZA-Kru SAV 20.764 -0.247Table S4.Statistics of CLASSIC simulated HFG against FLUXNET observations.RMSE (in W m -2 ) and R 2 are calculated as described in Section 5.1.site biome RMSE R 2 AU-Tum EBF 4.43 -0.83 FR-Pue EBF 5.657 -0.984 GH-Ank EBF 1.107 -2.434 MY-PSO EBF 1.881 -16.98 CA-Qfo ENF 5.768 -2.841 DE-Tha ENF 3.222 0.156 FI-Hyy ENF 3.193 0.634 IT-Lav ENF 5.399 -1.129 IT-SRo ENF 3.946 -0.624 NL-Loo ENF 2.532 -0.197 RU-Fyo ENF 5.693 -2.425 CA-TPD DBF 4.144 -0.097 DK-Sor DBF 3.004 0.331 US-WCr DBF 9.097 -1.209 ZM-Mon DBF 2.879 -0.915 DE-Kli CRO 6.463 0.24 ES-LgS OSH 8.141 -0.599 IT-Tor GRA 5.592 -0.801 RU-Ha1 GRA 14.062 -20.028US-Wkg GRA 2.404 0.702 RU-Che WET 16.869 -31.633RU-SkP DNF 12.185 -37.282SD-Dem SAV 2.192 0.081 ZA-Kru SAV 7.85 -0.043Table S5.Statistics of CLASSIC simulated GPP against FLUXNET observations.RMSE (in kg C m -2 month -1 ) and R 2 are calculated as described in Section 5.1.site biome RMSE R 2 AU-Tum EBF 0.14 -1.371 BR-Sa1 EBF 0.082 -0.291 FR-Pue EBF 0.053 -0.235 GF-Guy EBF 0.078 -3.641 GH-Ank EBF 0.063 -0.141 MY-PSO EBF 0.055 -5.062 CA-Qfo ENF 0.076 -0.244 CZ-BK1 ENF 0.042 0.847 DE-Tha ENF 0.037 0.902 FI-Hyy ENF 0.055 0.704 IT-Lav ENF 0.07 0.652 IT-SRo ENF 0.064 0.347 NL-Loo ENF 0.045 0.753 RU-Fyo ENF 0.044 0.859 CA-TPD DBF 0.053 0.838 DK-Sor DBF 0.055 0.884 FR-Fon DBF 0.06 0.805 US-WCr DBF 0.07 0.739 ZM-Mon DBF 0.087 -0.352 CG-Tch SAV 0.192 -3.422 SD-Dem SAV 0.09 0.026 ZA-Kru SAV 0.078 0.076 CN-Dan GRA 0.025 0.3 IT-Tor GRA 0.069 0.497 PA-SPs GRA 0.084 0.009 RU-Ha1 GRA 0.058 -0.039 US-Wkg GRA 0.035 -0.337 DE-Kli CRO 0.142 -0.28 ES-LgS OSH 0.019 0.581 RU-Che WET 0.047 0.115 RU-SkP DNF 0.042 0.118 Table S6.Statistics of CLASSIC simulated RECO against FLUXNET observations.RMSE (in kg C m -2 month -1 ) and R 2 are calculated as described in Section 5.1.site biome RMSE R 2 AU-Tum EBF 0.107 -0.707 BR-Sa1 EBF 0.103 -0.946 FR-Pue EBF 0.038 0.136 GF-Guy EBF 0.075 -0.565 GH-Ank EBF 0.103 -1.459 MY-PSO EBF 0.049 -1.335 CA-Qfo ENF 0.062 -0.343 CZ-BK1 ENF 0.086 -1.606 DE-Tha ENF 0.058 0.341 FI-Hyy ENF 0.066 -0.059 IT-Lav ENF 0.098 -5.146 IT-SRo ENF 0.061 0.177 NL-Loo ENF 0.068 -0.33 RU-Fyo ENF 0.046 0.796 CA-TPD DBF 0.115 -1.68 DK-Sor DBF 0.052 0.641 FR-Fon DBF 0.079 -1.726 US-WCr DBF 0.062 0.311 ZM-Mon DBF 0.096 -0.786 CG-Tch SAV 0.157 -4.783 SD-Dem SAV 0.039 0.22 ZA-Kru SAV 0.072 -0.03
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.529 | 0.161 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".