Advancing cooperative breeding research with a peer-reviewed and “live” Cooperative-Breeding Database (Co-BreeD)
Bibliographic record
Abstract
Abstract Research on cooperative breeding (a system with the core characteristic of individuals providing care for the offspring of others) is important for understanding sociality and cooperation. However, large-scale comparative analyses on the drivers and consequences of cooperation frequently use considerably inaccurate datasets (e.g. due to inconsistent definitions and outdated information). To advance comparative research on cooperative breeding, we introduce the Co operative- Bree ding D atabase (Co-BreeD), a growing database of key socio-biological parameters of birds and mammals. First, we describe Co-BreeD’s structure as a (i) sample-based (i.e. multiple samples per species linked to an exact sampling location and period), (ii) peer-reviewed and (iii) updatable resource. Respectively, these curating principles allow for (i) investigating intra- and inter-species variation and linking between fine-scale social and environmental parameters, (ii) accuracy and (iii) continuous correction and expansion with the publication of new data. Second, we present the first Co-BreeD dataset, which estimates the prevalence of breeding events with potential alloparents in 265 samples from 233 populations of 150 species, including 2 human societies (N = 26,366 breeding events). We conclude by demonstrating (i) how Co-BreeD facilitates more accurate comparative research (e.g. increased explanatory power by enabling the study of cooperative breeding as a continuous trait, and statistically accounting for the sampling error probabilities), and (ii) that cooperative breeding in birds and mammals is more prevalent than currently estimated.
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 | 0.039 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".