Distance decay 2.0 – a global synthesis of taxonomic and functional decay in ecological communities
Bibliographic record
Abstract
Datasets used in the analysis of the manuscript by Graco-Roza, C., Aarnio, S., Abrego, N., Acosta, A. T., Alahuhta, J., Altman, J., ... & Soininen, J. (2022). Distance decay 2.0–a global synthesis of taxonomic and functional turnover in ecological communities. Global Ecology and Biogeography. raw_data.zip - Includes the raw datasets used in the analysis. processed_data.xlsx - Includes the results from the distance decay analysis, specifically: - Dataset : dataset code (same as in raw_data) - Beta_type: The component of beta diversity (i.e., total similarity, replacement, richness differences) - Level : Taxonomic (TAX) or functional (FUN) - Based: Occurrence (occ) or Abundance (abund) - Organism: Code used to describe organisms (see Appendix S1 of the paper) - Realm: Aquatic, Terrestrial, or Freshwaters - Body_size - Dispersal_mode: Seeds, Passive or Active - Latitude: Mean latitude of the dataset (average of all data points) - Latitude_range Distance in kilometres between the two vertically most distant points. - Longitude_range: Distance in kilometres between the two horizontally most distant points. - spa_min: minimum distance between sites (in kilometres) - spa_mean: average distance between sites (in kilometres) - spa_max: maximum distance between sites (in kilometres) - ext: area in kilometres covered by all sites in the dataset - n_sites: Number of sites in each dataset - n_var: Number of environmental variables in each dataset - gamma_spe: Number of species observed in each dataset - gamma_trait: Volume of the hypervolume constructed using the traits in each dataset - n_traits: Number of traits in each dataset - Intercept_spa: Intercept of GLM including community similarity and spatial distances - Slope_Spa: Slope of GLM including community similarity and spatial distances - R2_spa: R² of GLM including community similarity and spatial distances - Intercept_env: Intercept of GLM including community similarity and environmental distances - Slope_env: Slope of GLM including community similarity and environmental distances - R2_env: R² of GLM including community similarity and environmental distances - Mantel_spa: Mantel statistics of community similarity and spatial distances - spa_signif: Significance of Mantel statistics considering community similarity and spatial distances - Mantel_env: Mantel statistics considering community similarity and environmental distances - env_signif: Significance of Mantel statistics considering community similarity and environmental distances Null_models.zip - Includes the results from the null models for each dataset.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.021 |
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".