Seed selection in the Java Sparrow (<i>Padda oryzivora</i>): preference and mechanical constraint
Bibliographic record
Abstract
Very few studies address the effect of hardness on seed selection in granivorous birds. As a defense against predators, plant species may produce seeds of various hardnesses, some of which are too hard for a bird to crack. Unsuccessful cracking attempts lead to loss of time and, thus, lowers energy-intake rate. Birds may prefer seeds with a short handling time and a large chance of being cracked. However, without knowing the maximal cracking force of the bird, it is difficult to distinguish between seed selection as a result of mechanical constraints and selection as a result of preference. Our experiments aimed to discriminate between these two effects. During two series of experiments, Java Sparrows (Padda oryzivora) were offered safflower seeds. Size characters and hardness of the seeds that remained after feeding were compared with a control group. Without prior experience, the birds showed selection as a result of mechanical constraints. Seeds were chosen randomly and only seeds with a hardness less than the maximal crushing force were eaten, with the rest being rejected. After some experience, birds started to actively select for seed size (e.g., depth) and preferred to eat the smallest seeds. Although the correlation between size and hardness is low, the birds successfully used size characteristics as a predictor for hardness.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".