Reducing agricultural loss and food waste: how will nature fare?
Bibliographic record
Abstract
With the global population currently over seven billion, and expected to increase to over nine billion in the next 30 years, the race is on to find ways to feed, water and clothe the citizens of the planet (United Nations, 2013). Food security is high on both national and international agendas (Gordon et al., 2012), with a push to increase the production of food by up to 70% in the next 30 years (Food and Agriculture Organization, 2013), and estimates of another one billion ha of land being converted to agriculture, mainly in the tropics (Tilman et al., 2001). The food security agenda may have obvious effects on wildlife species; however, some species may be affected by perverse outcomes that have not yet been assessed. Reduction in loss and waste from agricultural production and food systems (food waste) is one such issue. Here, we highlight the potential impact on species that have become reliant on food waste. These species may be seen currently as pests or vermin; however, the consequences of a reduction in food waste could not only affect them directly, but might also have significant cascading effects across food webs and impact on animal species of conservation importance.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".