Are great tits (<i>Parus major</i>) really optimal foragers?
Bibliographic record
Abstract
In this study, we test the classical prey model of optimal-foraging theory with great tits (Parus major) feeding on two types of mealworms presented on a conveyor belt. Contrary to the results of some previous experiments, prey types were given to birds in random order, therefore birds could not predict their next prey item. We tested birds' diet choices at four different prey-encounter rates. Our results show that in 95% of cases great tits consumed the more profitable prey type upon encounter. On the other hand, consumption of the less profitable prey type did not differ statistically from the "always-attack" strategy in 77% of cases when the rate of encounter with the more profitable prey was below a critical value, and did differ from that strategy in 67% of cases when the rate of encounter with the more profitable prey was above that critical value. Contrary to predictions of the classical prey model of optimal-foraging theory, our birds never completely excluded the less profitable prey type from their diet. We also estimated the functional responses of individual birds with respect to the more profitable prey type; birds' diet changes occurred too slowly to make these functional responses stabilizing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".