Bibliographic record
Abstract
Despite attempts at reconciliation, the role of omnivory in food web stability remains unclear. Here we develop a novel community matrix approach that is analogous to the bifurcation method of modular food web theory to show that the stability of omnivorous food chains depends critically on interaction strength. We find that there are only six possible ways that omnivorous interaction strengths can influence the stability of linear food chains. The results from these six cases suggest that: (1) strong omnivory is always destabilizing, (2) stabilization by weak to intermediate omnivorous interaction strengths dominates the set of possible stability responses, and, (3) omnivory can be occasionally strictly destabilizing or intermittently destabilizing. We then revisit the classical results of Pimm and Lawton to show that although their parameterization tends to produce a low percentage of stable omnivorous webs, the same parameterization shows strong theoretical support for the weak interaction effect. Finally, we end by arguing that our current empirical knowledge of omnivory resonates with this general theory.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".