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Record W2096635766 · doi:10.14430/arctic384

Breeding Season Survival of Female Lesser Scaup in the Northern Boreal Forest

2010· article· en· W2096635766 on OpenAlexafffundvenueabout
Rodney K. Brook, Robert G. Clark

Bibliographic record

VenueARCTIC · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Saskatchewan
FundersDelta WaterfowlInstitute for Wetland and Waterfowl Research, Ducks Unlimited CanadaUniversity of Saskatchewan
KeywordsAythyaBorealTaigaPredationBreedSeasonal breederEcologyPopulationBiologyPopulation declineNesting seasonDemography

Abstract

fetched live from OpenAlex

One hypothesis advanced to explain the decline in lesser scaup (Aythya affinis) populations during the past 20 years is that adult female survival has decreased. However, no survival probability estimates exist for the boreal forest, the region where most scaup breed. We captured and radio-marked female lesser scaup (n = 42) near Yellowknife, Northwest Territories, Canada, just before the breeding season in 1999 and 2000. Constant weekly survival probability was estimated using a Cormack-Jolly- Seber model (0.96). We extrapolate this rate to estimate survival probability for the nesting season (0.80, SE = 0.09), the period when females are at greatest risk of predation. Recent estimates of annual mortality (42%) suggest that about 50% of annual female mortality occurs during the breeding season, a result similar to recent conclusions from studies of prairie-nesting lesser scaup. Further, our survival estimate provides information required to produce preliminary models of population dynamics for boreal lesser scaup, a step that could greatly improve our understanding of decline in this species.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.767

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 communication0.0000.000
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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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 designObservational
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

Citations15
Published2010
Admission routes4
Has abstractyes

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