Bibliographic record
Abstract
Abstract For prey species to be successful, they must balance the conflicting demands of obtaining the resources necessary for survival while avoiding being killed by predators. This has a hierarchy of approaches that begins with avoiding dangerous (=predators) times and locations, using dangerous locations but adopting behaviours and tactics that assist prey in detecting predators first. If a predator detects them, they may also plan for this by staying in groups, thereby diluting the chance of being the individual that is killed or using coordinated group defence. Individuals may also employ toxins or specialised morphologies that deter a predator from attacking or assist in escape. Assuming that individual behaviour is not affected by the presence of parasites, prey may also escape predators after capture using specialised morphologies, and even ejecting limbs. Ultimately, captured prey may even try to attract other predators with the hope of escaping during the ensuing melee. Key Concepts Behavioural trade‐offs. The role of predators in affecting habitat quality. Costs and benefits of group living. The role of antipredator morphology. Batesian and Mullerian mimicry. The impact of parasites and how they can modify behaviour. Animal scaling, physics and its impact upon behaviour. The ecological importance of predators. Conservation ecology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".