A CLUTCH AND BROOD SURVIVAL MODEL THAT DISCRIMINATES RANDOM AND CORRELATED MORTALITY
Bibliographic record
Abstract
Quantitative conservation methodologies such as Population Viability Analysis (PVA) require reliable estimates of life history parameters such as breeding success. The utility of such metrics for egg-laying species is complicated by the fact that the mortality of eggs and juveniles can occur both randomly and independently over time, or catastrophically, as in the sudden loss of a clutch or brood. Not knowing the nature of mortality caused by either or both of abiotic (e.g., weather) and biotic (e.g., predation) events limits our ability to confidently assess a population's demography and sustainability, or rank competing hypotheses. To address this deficiency, we describe a statistical model that estimates egg and juvenile survival rates continuously from laying to fledging based on periodic observations of individual clutches and broods. Adjunct data on environmental or predation threats can be included in the model as covariate series potentially affecting juvenile survival. Our model can statistically characterize mortality between the extremes of random and catastrophic mortality and can determine if unwitnessed mortalities occurred independently or were correlated (i.e., overdispersed, where catastrophe is extreme overdispersion). Overdispersion is estimated as a parameter of the beta-binomial probability distribution of survival outcomes, which differs from its treatment in Program MARK where overdispersion is an a posteriori diagnostic referred to as ĉ. We used data for the sea duck Barrow's Goldeneye to illustrate our model. Specifically, we contribute to the argument that a larger brood confers a fitness advantage to a tending hen by concluding that brood size on hatch day is positively correlated with a juvenile's probability of surviving to fledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".