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NESTING EFFORT OF NORTHERN PINTAILS IN ALBERTA

2000· article· en· W2179264010 on OpenAlexafffundabout
Karla L. Guyn, Robert G. Clark

Bibliographic record

VenueOrnithological Applications · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of SaskatchewanDucks Unlimited Canada
FundersU.S. Fish and Wildlife ServiceInstitute for Wetland and Waterfowl Research, Ducks Unlimited CanadaUniversity of SaskatchewanMassachusetts Department of Fish and GameCalifornia Department of Fish and Game
KeywordsAnasAvian clutch sizeNesting (process)Nest (protein structural motif)ClutchPredationEcologyBiologyNesting seasonReproductive successParental investmentZoologyOffspringReproductionDemographyPopulationEngineering

Abstract

fetched live from OpenAlex

We studied nesting effort and success of Northern Pintails (Anas acuta) in southern Alberta. Annual nesting success estimates ranged from 6–18%. Clutch size averaged 7.2, and declined in a simple curvilinear fashion with nest initiation date. We found no relationship between egg size and clutch size or evidence from one year to the next of a trade-off between current and future investment in eggs. Within-year renesting rate ranged from 55%, based on a sample of 20 decoy-trapped females that lost their first nests to predators, to 85% based on a sample of 13 nest-trapped females forced to renest when we removed their clutches. Greater investment in initial clutches led to longer delays in laying replacement clutches. Because delays in renesting are costly (late-nesting females produce fewer offspring), females must contend with a trade-off between maximizing reproductive output in initial clutches versus the risk of delayed renesting if the first clutch should fail. We suggest that pintail reproductive traits have evolved primarily in response to short nesting seasons and variable environments.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.998

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)0.0100.003

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 head0.012
GPT teacher head0.243
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; both teacher heads agree on what is shown here.

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

Citations22
Published2000
Admission routes3
Has abstractyes

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