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Record W4243800689 · doi:10.2307/4089712

Spacing Patterns, Mating Systems, and Winter Philopatry in Harlequin Ducks

2000· article· en· W4243800689 on OpenAlexaff
Gregory J. Robertson, Fred Cooke, R. Ian Goudie, W. Sean Boyd

Bibliographic record

VenueThe Auk · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPhilopatryWaterfowlSeasonal breederCourtshipMatingBiologyMating systemEcologyHabitatZoologyDemographyBiological dispersalPopulation

Abstract

fetched live from OpenAlex

Many species of waterfowl form pair bonds during the nonbreeding season, yet current descriptions of mating systems and patterns of philopatry in waterfowl focus on the breeding grounds. We studied wintering Harlequin Ducks (Histrionicus histrionicus) in southwestern British Columbia to examine mating systems and philopatric behavior outside of the breeding season. The number of males at our study area was far more variable than that of females. Males that were unsuccessful in obtaining a mate were observed over a larger area than were paired males. Habitat use overlapped considerably among paired males, and no territorial boundaries were formed. Annual return rates to the nonbreeding grounds were high for both sexes (62% for females, 77% for males). Individuals not only returned to the general study area, but also to specific sections within the study area. Males that did not pair in the previous year returned to the study area in the subsequent year with the same frequency as males that had obtained mates; however, they tended to leave the study area prior to courtship. Therefore, it is possible that males return to the same wintering grounds each year to reunite with a previous mate. High levels of philopatry by both sexes allow pairs to reunite in fall, potentially increasing the benefits of philopatry for both sexes.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.997

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)0.0040.001

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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
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

Citations7
Published2000
Admission routes1
Has abstractyes

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