Supplement 1. Locations of field plots, plot-scale foliar chemical and morphological traits, results of leave-site-out and leave-year-out model cross-validations, and PLSR model coefficients.
Bibliographic record
Abstract
File List Plot_locations_foliar_traits.csv (MD5: 029b7ae2c45b10eadb5847e8a3871dc8) per_site_and_year_Crossvalidations.csv (MD5: 149187c633f7dd62ed27b6ff5754f967) PLS_coefficients_Mode_A_del15N.csv (MD5: 2ffb9111ceb8fd7920f6da687419349a) PLS_coefficients_Mode_A_M_area.csv (MD5: f5ae47c67b28a520057b463a9f7c9b6b) PLS_coefficients_Mode_A_pct_ADF.csv (MD5: 0f9326724c54568a894e8fb6019584e2) PLS_coefficients_Mode_A_pct_ADL.csv (MD5: d526a9843da4c29a20b56f3ac21fce95) PLS_coefficients_Mode_A_pct_C.csv (MD5: 72a41e0a101b92221995ec4328055257) PLS_coefficients_Mode_A_pct_Cellulose.csv (MD5: d0a6f931f835023c204fd774e25dad6b) PLS_coefficients_Mode_A_pct_N.csv (MD5: c3bb8a7499b0889cce803185c3e5771e) PLS_coefficients_Mode_B_del15N.csv (MD5: 028ed350afbb2dc8fba08b51e5919ac5) PLS_coefficients_Mode_B_M_area.csv (MD5: 5afa82d3e74680a3de9d545d29a6d6a5) PLS_coefficients_Mode_B_pct_ADF.csv (MD5: c0e67fdef81d55733ce5517d0c01e103) PLS_coefficients_Mode_B_pct_ADL.csv (MD5: cece7b51cd41edd4daf5fc5ace3e891e) PLS_coefficients_Mode_B_pct_C.csv (MD5: 0cd7afa80949a1c5b101b6abb08fb910) PLS_coefficients_Mode_B_pct_Cellulose.csv (MD5: 3ce2e034ee5dd3f4040d10665398bec5) PLS_coefficients_Mode_B_pct_N.csv (MD5: a2433d479202b1d6318b15456139f007) PLS_coefficients_Mode_C_del15N.csv (MD5: a40c8c1e5d6e74693c3c96170bb66470) PLS_coefficients_Mode_C_M_area.csv (MD5: 83fb7c81e4e54e5cce13fe1ab64c97f2) PLS_coefficients_Mode_C_pct_ADF.csv (MD5: 919fb19a5668c170ba34c6ab16821442) PLS_coefficients_Mode_C_pct_ADL.csv (MD5: 543d34f7126016c4c91b8c17ce5c930e) PLS_coefficients_Mode_C_pct_C.csv (MD5: 977ff98c013cc60871c559952d700b30) PLS_coefficients_Mode_C_pct_Cellulose.csv (MD5: 3ce2e034ee5dd3f4040d10665398bec5) PLS_coefficients_Mode_C_pct_N.csv (MD5: 530d8d631fc128b485097306816b4f0e) PLS_coefficients_Mode_D_del15N.csv (MD5: d15ae6acc62cd48a86561393086149cb) PLS_coefficients_Mode_D_M_area.csv (MD5: 63b2d2c9d5993b14e5f8d56f99c67c89) PLS_coefficients_Mode_D_pct_ADF.csv (MD5: 1722fb9dfc76be5d16d514e0c0ce37f1) PLS_coefficients_Mode_D_pct_ADL.csv (MD5: 9452b93bbd8996320799583526f2cac5) PLS_coefficients_Mode_D_pct_C.csv (MD5: 7aba2ab18097447adbec2d1593717a42) PLS_coefficients_Mode_D_pct_Cellulose.csv (MD5: 59c44ea1ddfedef39968b98b1297c489) PLS_coefficients_Mode_D_pct_N.csv (MD5: af16ca0e230b3fd91b3588ac7cc15fc8) Description Data stored in CSV files (comma delimited text files with header): “Plot_locations_foliar_traits.csv” Plot: Plot code (AK - Adirondacks NY, BH - Baraboo Hills WI, BI - Blackhawk Island WI, DC - Madison WI, GR - Green Ridge State Forest MD, IDS - Green Ridge State Forest MD, KM - Kettle Moraine State Forest WI, MN - Minnesota Arrowhead MN, NC - Chequamegon-Nicolet State Forest WI, OF - Ottawa National Forest MI, PB - Pine Barrens WI, PM - Porcupine Mountains WI, SF - Sylvania National Forest MI, SR - Savage River State Forest MD), Year: year of sampling, Latitude: geographic Y coordinates (WGS84 ellipsoid), Longitude: geographic X coordinates (WGS84 ellipsoid), spp1: Code, dominant species (see Table A2, Appendix A), rba1: relative basal area of spp1 (fraction), spp2: Code, co-dominant species (see Table A2, appendix A), rba2: relative basal area of spp2 (fraction), CAR: Carbon (mean of % by weight), CAR_sd: Carbon (standard deviation of % by weight), CEL: Cellulose (mean of % by weight), CEL_sd: Cellulose (standard deviation of % by weight), ADF: Acid detergent fiber (mean of % by weight), ADF_sd: Acid detergent fiber (standard deviation of % by weight), ADL: Acid detergent lignin (mean of % by weight), ADL_sd: Acid detergent lignin (standard deviation of % by weight), LMA: Leaf mass per area (mean g/m²), LMA_sd: Leaf mass per area (standard deviation g/m²), NIT: Nitrogen (mean of % by weight), NIT_sd: Nitrogen (standard deviation of % by weight), N15: δ15N (mean of ‰ measured), N15_sd: δ15N (mean of ‰ measured). “per_site_and_year_Crossvalidations.csv” Results of leave-site-out and leave-year-out cross-validations for partial least squares models presented in manuscript. Rows are indexed by either year or site (see above for site codes). For each trait (Nitrogen, del15N, Marea, Cellulose, Carbon, ADL and ADF) columns indicate: RMSE cal.: Root mean squared error, calibration; RMSE val.: Root mean squared error, validation; % in Pred. Int.: Percent observations within prediction intervals; % of range: validation RMSE as percent of range of observations; N cal.: Number of calibration samples; N val.: Number of validation samples. All files in supplement 2 (e.g., ”PLS_coefficients_Mode_A_del15N.csv”) are coefficients obtained from 500 randomized replicates of PLSR models described in the manuscript. The “Mode” (i.e., “Mode A” in example above) indicates models built using canopy traits aggregated using canopy weighting schemes described in Table A3, Appendix A. All files are suffixed with the respective response variables (del15N: δ15N [‰], M_area: Marea [g/m²], pct_ADF: ADF [%], pct_ADL: ADL [%], pct_C [%], pct_Cellulose: Cellulose [%], pct_N: Nitrogen [%]). Rows correspond to model replicates (indexed by column “Model”), columns correspond with PLSR model coefficients at nominal wavelengths (i.e., column WVL_453 = PLSR model coefficient for wavelength 453nm.) Columns filled with zeros indicate noise-contaminated (< 414 nm and > 2408 nm) or water absorption bands. Column “Intercept” is the PLSR model constant term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.784 | 0.319 |
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".