An integrative, peer‐reviewed and open‐source cooperative‐breeding database (Co‐ <scp>BreeD</scp> )
Bibliographic record
Abstract
Large-scale, cross-species comparative analyses on cooperative breeding-where individuals care for the offspring of other group members-are important for understanding sociality and cooperation. However, the datasets that facilitate these analyses are often limited in precision. To advance comparative research on cooperative breeding, we hereby introduce the Cooperative-Breeding Database (Co-BreeD) for birds and mammals. We describe key features of Co-BreeD's structure: (i) integration of complementary datasets, each presenting a biological parameter relevant to cooperative-breeding research; (ii) sample-based (i.e. multiple samples per species linked to an exact sampling location and period); and (iii) open-source. Respectively, these features enable: (a) comprehensive identification of cooperative-breeding species according to the user's chosen definition, (b) linking intra- and inter-specific variation in traits with fine-scale environmental parameters and (c) enabling the research community to correct and expand this database. We present the initial Co-BreeD dataset, which estimates the prevalence of breeding events involving potential alloparents in 460 populations of 324 species, including 6 human populations (No. total = 43,247 breeding events). We conclude by demonstrating: (i) how Co-BreeD can improve comparative research (e.g. by enabling the study of cooperative breeding as a continuous rather than a binary trait); and (ii) that cooperative breeding is probably more prevalent than previously estimated in birds and mammals.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.073 |
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".