A global inventory of animal diversity measured in different grazing treatments
Bibliographic record
Abstract
Grazing by wild and domesticated grazers occurs within many terrestrial ecosystems worldwide, with positive and negative impacts on biodiversity. Management of grazed lands in support of biological conservation could benefit from a compiled dataset of animal biodiversity within and adjacent to grazed sites. In this database, we have assembled data from the peer-reviewed literature that included all forms of grazing, co-occurring species, and site information. We reviewed 3,489 published articles and found 245 studies in 41 countries that surveyed animal biodiversity co-occurring with grazers. We extracted 16,105 observations of animal surveys for over 1,200 species in all terrestrial ecosystems and on all continents except Antarctica. We then compiled 28 different grazing variables that focus on management systems, assemblages of grazer species, ecosystem characteristics, and survey type. Our database provides the most comprehensive summary of animal biodiversity patterns that co-occur with wild and domesticated grazers. This database could be used in future conservation initiatives and grazing management to enhance the prolonged maintenance of ecosystems and ecosystem services.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.025 |
| 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) | 0.005 | 0.002 |
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".