NEST SURVIVAL IN DUSKY CANADA GEESE (BRANTA CANADENSIS OCCIDENTALIS): USE OF DISCRETE-TIME MODELS
Bibliographic record
Abstract
The Dusky Canada Goose (Branta canadensis occidentalis) population that breeds in the Copper River Delta, Alaska, has declined substantially since the late 1970s. Persistent low numbers have been attributed to low productivity in recent years. We examined patterns in survival rates of 1,852 nests to better understand ecological processes that influenced productivity during 1997-2000. We compared 10 nonparametric models of daily survival rate of nests (DSR) that included variation among years, calendar dates, nest initiation dates, and nest ages with equivalent models based on parametric functions. The unequivocal best model included patterns of DSR that varied among discrete periods of years, calendar dates, and nest ages. Generally, DSR was low early in the nesting season and higher midseason. Across years, patterns in DSR were most variable early and late in the nesting season. Daily survival rates of nests declined between the first and second week after initiation, increased until the fourth week, and then declined during the last week before hatch. Nest survival probability estimates ranged from 0.07 to 0.71 across years and nest initiation dates. Mean rates of nest survival ranged between 0.21 and 0.31 each year. We suggest (1) considering models that do not limit estimates of daily nest survival to parametric forms; (2) placing greater emphasis on sample size when nests are rare, to obtain accurate estimates of nest survival; and (3) developing new techniques to estimate the number of nests initiated. Supervivencia de los Nidos en Branta canadensis occidentalis: Uso de Modelos de Tiempo Discreto
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".