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Record W2117246153 · doi:10.1093/beheco/arq032

Determinants of false alarms in staging flocks of semipalmated sandpipers

2010· article· en· W2117246153 on OpenAlexaff
Guy Beauchamp

Bibliographic record

VenueBehavioral Ecology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCalidrisFlockBiologySandpiperPredationOddsFalse positive paradoxZoologyEcologyDemographyStatisticsLogistic regressionMathematics

Abstract

fetched live from OpenAlex

False alarms occur when animals flee abruptly upon detection of a threat that subsequently proved harmless. False alarms are common in many species of birds and mammals and account for a surprisingly high proportion of all alarms. False alarms are expected to be more frequent in larger groups, where the odds of misclassifying threats are higher, and under environmental conditions where detection of threats is compromised, such as low light levels. In addition, false alarms should be less frequent when the energetic cost of fleeing increases. I examined these hypotheses in roosting flocks of staging semipalmated sandpipers (<it>Calidris pusilla</it>) over 2 years. False alarms increased with group size but the effect of group size was confounded by the fact that more attacks by falcons (<it>Falco</it> spp.) were directed at larger roosts. False alarms were more frequent at low light levels and later during staging. As individuals double their body mass during staging, the energetic cost of fleeing must greatly increase thus contributing to decreased responsiveness. A simple reduction in responsiveness caused by repeated exposures to harmless signals would also produce a temporal decrease in responsiveness but this hypothesis cannot account for the effect of group size and light level. Study of the determinants of false alarms provides an opportunity to examine adjustments in behavior in relation to changes in perceived predation risk.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.475

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)0.0000.000

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 head0.024
GPT teacher head0.286
Teacher spread0.262 · 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

Citations34
Published2010
Admission routes1
Has abstractyes

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