Data from: Human-dominated land uses favour species affiliated with more extreme climates, especially in the tropics
Bibliographic record
Abstract
The data are derived from the PREDICTS Project database (https://data.nhm.ac.uk/dataset/902f084d-ce3f-429f-a6a5-23162c73fdf7) used within the paper titled "Human-dominated land uses favour species affiliated with more extreme climates, especially in the tropics" (DOI: 10.1111/ecog.04806). If using this data, please make sure that the PREDICTS Project is also cited. For Williams_et_al_Ecography_data_CWM -> These data include, for the species assemblages in the analyses, the raw community weighted means (CWMs) for the extreme (maximum or minimum) and range-wide variation (standard deviations) for each climatic niche property. The columns for the CWMs of the extremes are headed CWM_MAXTmax, CWM_MINTmin, CWM_MAXPpmax and CWM_MINPpmin to match the terms used in the paper. The columns for the CWMs of range-wide variation are headed CWM_STDTmax, CWM_STDTmin, CWM_STDPpmax and CWM_STDPpmin. The columns headed 'Total_abundance' and 'Species_richness' were calculated during data preparation. The other columns included are details about each site, unchanged from the PREDICTS Project database. For Williams_et_al_Ecography_data_Abundance_Tmax_Tropics, Williams_et_al_Ecography_data_Abundance_Tmax_Temperate, Williams_et_al_Ecography_data_Abundance_Ppmin_Tropics and Williams_et_al_Ecography_data_Abundance_Ppmin_Temperate -> These are the data used for the abundance analyses. There are four files within each of these files, one for each species group (formed by splitting around the medians of the exteme and standard deviation of the climatic variable in question, i.e. either maximum temperature of the hottest month or precipitation of the driest month). 'Tropics' or 'Temperate' within the file name denote whether the data are from from tropical or temperate latitudes, respectively. The column headed 'LogAbund' includes the log(x+1) transformation of the abundance measures, used within the models. The columns headed 'Simpson_diversity', 'ChaoR', 'Richness_rarefied', 'UseIntensity' and 'UI' are other variables we calculated along the way, but did not use in these analyses. The column 'LandUse' contains our slight alterations (e.g. changing vegetation to Vegetation) of the 'Predominant_land_use' column found within the PREDICTS Project database. The columns headed 'Total_abundance' and 'Species_richness' were calculated by us using the PREDICTS Project database. The other columns include unchanged species-level data from the PREDICTS Project database.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.182 | 0.106 |
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".