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Record W6967233310 · doi:10.5061/dryad.c54h30c

Data from: Parameterizing the robust design in the BUGS language: lifetime carry‐over effects of environmental conditions during growth on a long‐lived bird

2018· dataset· en· W6967233310 on OpenAlexaboutno aff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typedataset
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsBayesian probabilityPopulationMaximum likelihoodBayesian inferenceMark and recaptureAnatidae

Abstract

fetched live from OpenAlex

1. Since the initial development of the robust design, this capture‐recapture model structure has been modified to estimate temporary emigration, and expanded to include auxiliary information such as band recovery and live resight data using maximum likelihood approaches. These developments have allowed investigators to separately assess individual and group effects on true survival, site fidelity, and temporary emigration. Additionally, recent advances in the BUGS language have allowed researchers to develop increasingly complex, user‐specified models in Bayesian frameworks. 2. The robust design has rarely been implemented in the BUGS language, and previous attempts to parameterize the robust design in BUGS exhibited strong bias in estimates of temporary emigration rates. Given the limitations of current parameterizations of the robust design in Bayesian frameworks, and our research objectives, we have developed a parameterization of the robust design in the BUGS language that produces unbiased estimates of all model parameters. 3. We use this novel model structure to examine lifetime carry‐over effects of environmental conditions during early life on annual breeding probabilities of Pacific black brent (Branta bernicla nigricans) breeding on the Yukon‐Kuskokwim River Delta in western Alaska. We found that individuals that were more structurally developed as goslings bred at increased rates as adults (β = 0.14, f = 0.94), with no effect on adult survival (β = 0.01, f = 0.62). Additionally, we provide evidence for long‐term declines in apparent survival of breeding adult females at the population level (β = ‐0.01, f = 0.90). 4. This novel model structure can be easily expanded (Gibson et al., in review), and has important implications for population modelling at broad scales, where we apply it to a declining population of Pacific black brent. Given long‐term declines in gosling growth on the Yukon‐Kuskokwim Delta, we predict future declines in population trajectories as a result of lifetime carry‐over effects of environmental conditions during growth on adult fecundity, and long‐term declines in adult survival.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0040.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.251
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreDataset

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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