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Record W2098664998 · doi:10.1139/z09-135

Cross-seasonal dynamics in body mass of male Harlequin Ducks: a strategy for meeting costs of reproduction

2010· article· en· W2098664998 on OpenAlexafffundvenue
Daniel Esler, Jeanine C. Bond

Bibliographic record

VenueCanadian Journal of Zoology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsSimon Fraser University
FundersSimon Fraser UniversityBC Hydro
KeywordsWaterfowlReproductionBiologySTREAMSSeasonal breederEcologySeasonalityNutrientAnatidaeZoologyFisheryHabitat

Abstract

fetched live from OpenAlex

Considerations of acquisition of energy for reproduction by waterfowl have disproportionately focused on females, although males also require energy for reproduction. We quantified variation in body mass of male Harlequin Ducks ( Histrionicus histrionicus (L., 1758)) on coastal wintering areas prior to spring migration, as well as on breeding grounds, to determine when and where nutrients were acquired to meet costs of reproduction. Male mass on wintering grounds increased, on average, by 45 g (7%) in the weeks prior to migration. On breeding streams, we inferred that body mass of paired males decreased with the length of time on breeding grounds. Also, on average, male mass was considerably lower on breeding streams than when they departed coastal wintering sites. We conclude that males store nutrients on marine wintering grounds for subsequent use during the breeding season. Male Harlequin Ducks are highly vigilant while on breeding streams and the associated reduction in feeding time presumably requires energy stores. We suggest that males have evolved a strategy that is at least partially “capital” for meeting costs of reproduction, in which they acquire an optimal amount of energy reserves prior to spring migration and subsequently invest them in behaviours that can enhance reproductive success.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.258
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2010
Admission routes3
Has abstractyes

Explore more

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