What Can Nest-Building Birds Teach Us?
Bibliographic record
Abstract
For many years nest building in birds has been considered a remarkable behaviour.Perhaps just as remarkable is the public and scholarly consensus that bird nests are achieved by instinct alone.Here we take the opportunity to review nearly 150 years of observational and experimental data on avian nest building.As a result we find that instinct alone is insufficient to explain the data: birds use information they gather themselves and from other individuals to make nest-building decisions.Importantly, these data confirm that learning plays a significant role in a variety of nest-building decisions.We outline, then, the multiplicity of ways in which learning (e.g., imprinting, associative learning, social learning) might act to affect nest building and how these might help to explain the diversity both of nest-building behaviour and in the resulting structure.As a consequence, we contend that nest building is a much under-investigated behaviour that holds promise both for determining a variety of roles for learning in that behaviour as well as a new model system for examining brain-behaviour relationships.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".