FOREBRAIN SIZE AND INNOVATION RATE IN EUROPEAN BIRDS: FEEDING, NESTING AND CONFOUNDING VARIABLES
Bibliographic record
Abstract
Abstract Previous work has shown a positive correlation between relative forebrain size and feeding innovation frequency, corrected for species number, over different taxonomic groups of birds. Several confounding variables could account for this relationship: ornithologists could notice and report innovations more often in certain taxa because of biased expectations, greater research effort, editorial bias in journals or large population sizes of the taxa. The innovationforebrain correlation could also be spuriously caused by phylogeny or juvenile development mode. We examined these possibilities by entering species number per taxon, population size, number of full length papers, expectations (assessed by a questionnaire), journal source and development mode in multiple regressions that also included relative forebrain size. We did this with and without phylogenetic corrections and tested two behavioural categories, feeding and nesting, where flexibility and learning are clearly thought to differ, but confounds should have similar effects. Through an exhaustive survey covering 30 years in 11 journals, a total of 683 innovations was gathered for the northwestern part of Europe, 507 for feeding and 176 for nesting. Species number per taxon was the only significant confound for both feeding and nesting reports; as predicted, forebrain size was a second significant predictor for feeding innovations, but not for nesting. The frequency of feeding innovations in the short notes of ornithology journals thus appears to be a valid and reliable way to operationalise behavioural flexibility in birds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".