A cross-taxonomic explanatory framework for mobbing behavior
Bibliographic record
Abstract
Mobbing is an important antipredator strategy wherein prey approach harass and attack nonhunting predators, using conspicuous stereotyped movements and/or vocalizations. This behavior can reduce current and future threats of predation. In this paper, we aim to provide a framework that integrates prey, predator, and environmental factors, to illuminate how multiple factors and their interactions can explain mobbing propensity. We hope to encourage targeted and systematic investigation into the ecology and evolution of mobbing by focusing on an integrated view on life history, social, and ecological conditions, and a broader taxonomic spread of investigations. By incorporating a broader view of an animal's ecology, we can better understand the tradeoff that individuals experience when deciding to engage in mobbing, and by examining this behavior across different species, life-histories, ecologies, and communities, we can better understand the larger ecological contexts in which mobbing is an effective strategy as opposed to when it is not. Finally, we highlight some other areas we feel need further investigation to advance our understanding of mobbing.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".