A General Bayesian Hierarchical Model for Estimating Survival of Nests and Young
Bibliographic record
Abstract
Models for estimating survival probability of nests and young have changed dramatically since the development of the Mayfield method. Improvements in software and a steady increase in computing power have allowed more complexity and realism in these models, allowing researchers to provide better estimates of survival and to relate survival rates to relevant covariates. However, many current analysis methods utilize fixed-effects models with the implicit assumption that the covariates explain all of the variation in the data, other than random variation within a specified family of distributions. This is generally a strong assumption, and, in the presence of heterogeneity and lack of independence, these estimates have been shown to be negatively biased. Others have begun to explore random-effects models for these situations, but a readily applicable Bayesian approach has been lacking. We present a general Bayesian modeling framework appropriate for survival of both nests and young that simultaneously allows for the inclusion of individual covariates and random effects and provides a measure of goodness-of-fit. We used previously published data on survival of Common Goldeneye (Bucephala clangula) ducklings in interior Alaska and on nest survival in three species of prairie-nesting ducks that nested in the Missouri Coteau region of North Dakota to demonstrate this approach. The inclusion of a brood-level random effect in the Common Goldeneye example increased point estimates and credible interval [CI] coverage from 0.62 (95% CI: 0.49–0.73) and 0.66 (95% CI: 0.58–0.74) for 2002 and 2003, respectively, to 0.69 (95% CI: 0.42–0.88) and 0.74 (95% CI: 0.57–0.88) for 2002 and 2003, respectively.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".