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Record W6980906586

Delayed Nesting by Female Canada Geese (Branta Canadensis): Benefits and Costs

2012· article· en· W6980906586 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons - USU (Utah State University) · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHatchlingNesting (process)FledgeNest (protein structural motif)PopulationCompetition (biology)
DOInot available

Abstract

fetched live from OpenAlex

In many avian species, females do not nest the first year they attain sexual maturity. I examined the benefits and costs of delayed nesting in a nonmigratory population of Canada Geese (Branta canadensis) in New Haven County, Connecticut, from 1984 through 2008. I individually marked 381 female goslings and monitored them throughout their lives. Eighty-seven females were recruited into the local breeding population; 16 of these started nesting when 1 or 2 years old (young nesters), and 71 started nesting when 3 to 9 years old (delayed nesters). During their first reproductive effort, young nesters and delayed nesters produced similar-sized clutches but young nesters produced fewer hatchlings or fledglings. Young nesters died sooner than delayed nesters, but the two groups were similar in number of years of life following first nesting effort, number of nesting years during life span, and total lifetime production of eggs, hatchlings, and fledglings. Both young nesters and delayed nesters had similar values of λ(m), which is an integrated measure of an individual's propensity fitness. Young nesters weighed more at fledging than delayed nesters, which suggests that larger and healthier females were more likely to become young nesters. Competition among Canada Geese for safe nesting sites on islands was keen in the study area. This may have contributed to the prevalence of delayed nesting because geese that were unable to secure a safe nesting site may have delayed nesting until the following year.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.167
Teacher spread0.142 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2012
Admission routes1
Has abstractyes

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