A safe agricultural space for biodiversity
Bibliographic record
Abstract
Agriculture is the main driver of the rapid collapse of biodiversity, upon which all life on Earth, including agricultural production, depends. As we face the challenge of feeding a growing human population under a changing climate regime, the pressure on biodiversity is expected to further intensify. While the potential to expand and improve natural habitats for biodiversity conservation has been widely explored in large-scale scenarios of agricultural systems, the critical role of agricultural landscapes’ management on halting the loss of biodiversity remains unexplored at this scale. We argue that, to achieve an effective conservation of biodiversity (both natural and agricultural), the combined multivariate effects of agriculture on biodiversity must be accounted for, including its surface area as well as its management. Based on a literature review, we identified the main biodiversity pressures stemming from agriculture: land-use change, contribution to climate change, water withdrawal, pesticide pollution, nutrient (nitrogen and phosphorus) pollution, and landscape and farm-scale simplification (of croplands and pastures). For each one, we proposed a critical boundary, based on reviews of studies covering a range of taxa, biodiversity metrics, and biomes, below or above which negative impacts on biodiversity are minimized or positive effects arise. Implemented simultaneously, the identified boundaries would integrate biodiversity conservation within and across farmlands and minimize agriculture’s far-reaching impacts on biodiversity. We present a framework called “agricultural boundaries for biodiversity” that will allow to explore the potential of developing agricultural systems that effectively reconcile food production and biodiversity conservation at large scales.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".