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Record W4414200134 · doi:10.1080/15594491.2025.2553423

A confirmed case of social polygyny in Eastern Whip-poor-will ( <i>Antrostomus vociferus</i> )

2025· article· en· W4414200134 on OpenAlexaboutno aff
Asch M. Nighthawk, Kristen M. Malone, Jacob N. Straub, Matthew D. Palumbo

Bibliographic record

VenueThe Wilson Journal of Ornithology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersAmerican Ornithological SocietyNorthern New York AudubonNew York State Department of Environmental Conservation
KeywordsPolygynyPopulationSocial organisation

Abstract

fetched live from OpenAlex

Birds that are typically socially monogamous occasionally engage in social polygyny. By investigating the circumstances and consequences of atypical social polygyny, we can expand knowledge on the flexibility of their social systems. The Eastern Whip-poor-will (Antrostomus vociferus) is a socially monogamous nightjar that breeds in the northeastern USA and eastern Canada. We studied Eastern Whip-poor-will nesting ecology within the alvar barrens regions of Jefferson County, NY, USA, by tracking individuals to nests with ground-based telemetry during the 2024 breeding season. After locating nests, we monitored them with continuous-recording video cameras. In July 2024, through a combination of telemetry tracking, video cameras, and direct observations, we confirmed that a radio-tagged male was paired with and concurrently attending nests of two females. A neighboring male had died earlier in the breeding season, which may have contributed to this instance of social polygyny. Nest camera data suggest that the male involved in polygyny reduced parental investment in at least one of the nests during egg incubation. Although the main social system of Eastern Whip-poor-wills is social monogamy, this case of social polygyny indicates that they may adopt non-monogamous systems in response to unexpected events during the breeding season.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.144

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.253
Teacher spread0.223 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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