EWINA_RICH : a database of EarthWorm native and alien species richness accross North America
Bibliographic record
Abstract
EWINA_RICH gathers data on observed and predicted native and alien species richness of earthworms species across geographical units of North America (Mexico, US and Canada), based on data from 1850 to 2021. Please refer to the published paper for the details about the process to produce the predictions and the general interpretation of the results. Data are given at two distinct spatial resolutions - Data at the resolution of counties or equivalent EWINA_counties.geojson: Spatial layer of all counties or alike geographical units, with environmental covariates. Used to map geographical units and to predict RASR. EWINA_2000_counties_obs.csv: Observed earthworm species richness and RASR (Relative Alien Species Richness) in the geographical units with earthworm data, since year 2000, together with the environmtal covariates.(Coverage based estimates, used in the paper, will be released soon, feel free to reach out if you need them). EWINA_2000_counties_pred.csv: Predicted earthworm RASR (Relative Alien Species Richness) and its uncertainty, in all counties or equivalent, based on a model fitted on data after the year 2000. - Data at the resolution of TDWG4 geographical units (≈ states) see the Biodiversity Information Facility Website for more info about the definition of TDWG4 geographical units EWINA_TDWG4_aboveground.geojson: Spatial layer of TDWG4 geographical units, with above ground alien taxa richness from Dawson 2017 https://doi.org/10.1038/s41559-017-0186. EWINA_TDWG4_earthworms_YYYY.csv: Observed earthworm native and exotic species richness in the TDWG4 units, data cumulated from 1850 to YYYY. EWINA_TDWG4_fun.csv: Observed native and alien earthworm species functionnal role in the TDWG4 units, data cumulated from 1850 to 2021. All data files are provided with a readme file that explains the meaning of the variables. Scripts to use the data are stored on GitHub: https://github.com/JeromeMathieuEcology/GlobalWorming
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".