Data from: The mechanics of predator-prey interactions: first principles of physics predict predator-prey size ratios
Bibliographic record
Abstract
1. Robust predictions of predator-prey interactions are fundamental for the understanding of food webs, their structure, dynamics, resistance to species loss, response to invasions and ecosystem function. Most current food web models measure parameters at the food web level to predict patterns at the same level. Thus, they are sensitive to the quality of the data, and may be ineffective in predicting non-observed interactions and disturbed food webs. There is a need for mechanistic models that predict the occurrence of a predator-prey interaction based on lower levels of organization (i.e., the traits of organisms) and the properties of their environment. 2. Here, we present such a model that focuses on the predation act itself. We built a Newtonian, mechanical model for the processes of searching, capturing and handling of a prey item by a predator. Associated with general metabolic laws, we predict the net energy gain from predation for pairs of pelagic or flying predator species and their prey depending on their body sizes. 3. Predicted interactions match well with data from the most extensive predator-prey database, and overall model accuracy is greater than the allometric niche model. 4. Our model shows that it is possible to accurately predict the structure of food webs using only a few mechanical traits. It underlines the importance of physical constraints in structuring food webs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.038 |
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".