The repeatability of behavioural laterality during nest building in zebra finches
Bibliographic record
Abstract
Cerebral laterality is a widespread phenomenon across animals and refers to the specialization of the left and right hemispheres of the brain for perceptual, cognitive and behavioural tasks. Behavioural laterality occurs in several contexts, including foraging, mate selection, predator detection and tool manufacture. Behavioural laterality during nest building, however, has rarely been addressed. We conducted two experiments to examine (1) whether behavioural laterality occurs during nest building, (2) whether laterality correlates with nest-building speed, (3) whether laterality during nest building is repeatable, and (4) whether nest-building experience influences laterality. In Experiment 1, we scored individual laterality indices for 58 zebra finch (Taeniopygia guttata) males, the nest-building sex in this species, based on which eye he used to view then select the first 25 pieces of nest material. We calculated correlations between laterality strength and nest-building duration. In Experiment 2, to test the repeatability of laterality during nest building, we measured laterality for 20 males across five nests built by each male. Individuals varied both in the direction and the strength of behavioural laterality of material selection during nest building. Overall, however, males were not consistent in their laterality across the five nests. We found no correlation between laterality strength and nest-building duration in either experiment. Finally, we found evidence for building experience influencing the behavioural laterality of individuals: more building experience results in more predictable behavioural laterality during nest-material selection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".