Social learning about construction behaviour via an artefact
Bibliographic record
Abstract
One source of public information may be the enduring products of others' behaviour, such as discarded tools or vacated nests. Here, we examined whether observation of a nest affects the material captive zebra finch males prefer when they construct their first nest. It does: for first-time nest construction, males that viewed only an empty cage preferred the colour of material each initially favoured but those males that had observed a pre-built nest of material of their non-preferred colour lost their material-colour preference altogether. Additionally, half of the males that viewed a nest were tested in an environment (the laboratory) different to that in which they were reared (an outdoor aviary). We had expected the aviary-reared (versus laboratory-reared) males would be more uncertain, and thus more likely to select material for their first nest that matched in colour to the colour of the 'demonstrated' nest-but this was not the case. The aviary-reared males did, however, tend to touch first the demonstrated colour of material more than did the laboratory-reared males. Together these results show that both observation of a nest and a change in environment can influence the material choices of novice builders. For naïve animal builders, then, construction artefacts can be information resources for learning about potential construction material.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".