A global inventory of animal biodiversity measured in different grazing treatments
Bibliographic record
Abstract
Rangelands occupy more than half of terrestrial land cover and much of these lands are used for domestic grazing. Patterns of domestic grazing have been shifting recently, becoming more common in areas such as Brazil, Mexico, and South-East Asia. These are also correspond with some of biodiversity hotspots of the world. The effects of domestic grazing on biodiversity can be complex. Moreover, there is a important component of food security to be considered with changing grazing patterns. While there has been recent work quantifying the effects of domestic grazing on carbon emissions and plant composition, there has yet to be a detailed synthesis of grazing effects on animal diversity. Using a systematic review of the literature, we generated a database inventorying animal biodiversity in lands with domestic grazing, wild grazers, and without grazing. Information provided includes species-specific responses to grazing, measurements of grazing intensity (e.g., animals per hectare, biomass removed), grazer species, and geospatial coordinates. Our hopes is that this database could be used for sustainable grazing practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".