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Record W2048735282 · doi:10.1139/z10-044

Reproduction management affects breeding ecology and reproduction costs in feral urban Pigeons (<i>Columba livia</i>)

2010· article· en· W2048735282 on OpenAlexvenueno aff
Lisa Jacquin, Bernard Cazelles, Anne-Caroline Prévôt-Julliard, Gérard Leboucher, Julien Gasparini

Bibliographic record

VenueCanadian Journal of Zoology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsBiologyReproductionHatchingLimitingEcologyZoology

Abstract

fetched live from OpenAlex

Reproduction management of natural popsulations can have long-term consequences that have to be examined to avoid unwanted side effects. Management policies of urban Pigeons ( Columba livia Gmelin, 1789) include the set up of public Pigeon houses that aim at limiting hatching rate by egg removal. However, long-term consequences of this management method on the ecology of this species are still unknown. In this study we examined how egg removal affected egg-laying cycles of Pigeons by using a powerful method of time-series analysis, the wavelet method. We compared egg-laying cycles in Pigeon houses exposed to different management treatments and found that egg-laying cycles were shorter (4 weeks) in Pigeon houses with egg removal compared with control Pigeon houses without egg removal (11 weeks), suggesting that Pigeons respond to egg-removal pressure by multiplying reproduction attempts. Furthermore, we found that egg quality, an important index of female condition, was negatively affected by egg removal. This result suggests that the observed increase of egg production can lead to an increase of reproductive physiological costs and to a decrease of female condition. This study raises issues about potential consequences of such a management procedure on parasite resistance and health status of urban bird populations.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.778

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations29
Published2010
Admission routes1
Has abstractyes

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