Weighing the importance of animal body size in traditional food systems
Bibliographic record
Abstract
Traditional food systems based on harvest from the local environment are fundamental to the well-being of many communities, but their security is challenged by rapid socio-ecological change. We synthesized literature and data describing how a fundamental form of biodiversity, animal body size, contributes to the security of traditional food systems through relationships with species availability, accessibility, adequacy, and use. We found larger vertebrate species were more available, accessible, and used on a per kilogram basis, particularly for mammals. Conversely, larger species were no more or less adequate from a combined nutritional, health, and cultural perspective. Larger species represented more biomass, and this biomass required less time to harvest, with greater but more variable mean caloric returns over time. Smaller species provided more consistent caloric returns and were harvested during documented shortages of prey. This reliance on species with a range of body sizes is consistent with optimal foraging theory and the evolutionary value of flexibility, and highlights the importance of a biodiverse pool of species for traditional food security in times of change. Our synthesis of published literature and data highlights the many socio-ecological correlates of species size and how these relate to the security of traditional food systems.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".