The Primate Life History Database: a unique shared ecological data resource
Bibliographic record
Abstract
The importance of data archiving, data sharing, and public access to data has received considerable attention. Awareness is growing among scientists that collaborative databases can facilitate these activities.We provide a detailed description of the collaborative life history database developed by our Working Group at the National Evolutionary Synthesis Center (NESCent) to address questions about life history patterns and the evolution of mortality and demographic variability in wild primates.Examples from each of the seven primate species included in our database illustrate the range of data incorporated and the challenges, decision-making processes, and criteria applied to standardize data across diverse field studies. In addition to the descriptive and structural metadata associated with our database, we also describe the process metadata (how the database was designed and delivered) and the technical specifications of the database.Our database provides a useful model for other researchers interested in developing similar types of databases for other organisms, while our process metadata may be helpful to other groups of researchers interested in developing databases for other types of collaborative analyses.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".