MétaCan
Menu
Back to cohort
Record W4221018780 · doi:10.1163/1568539x-bja10147

Anatomy of avian distress calls: structure, variation, and complexity in two species of shorebird (Aves: Charadrii)

2022· article· en· W4221018780 on OpenAlexaff
Edward H. Miller, Kristal N. Kostoglou, David R. Wilson, Michael A. Weston

Bibliographic record

VenueBehaviour · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCharadriusZoologyVariation (astronomy)Interspecific competitionBiologyGeographyEcologyCommunicationPsychology

Abstract

fetched live from OpenAlex

Abstract Birds often vocalize when threatened or captured by a predator. We present detailed qualitative analyses of calls from 24 red-capped plover (Charadrius ruficapillus) and 117 masked lapwing (Vanellus miles) chicks (Charadriidae) that we recorded during handling. Calls were structurally complex and differed between species. Calls showed moderate structure at higher levels of organization (e.g., similarity between successive calls; sequential grading). Some call characteristics resembled those in other bird species in similar circumstances (e.g., in nonlinear phenomena). Most calls consisted of several different parts, which combined in different ways across calls. Past studies have overlooked most features of distress calls and calling in charadriids due to small sample sizes and limited spectrographic analyses. Understanding interspecific patterns in call structure, and determination of call functions, will require: detailed knowledge of natural history; detailed behavioural descriptions, acoustic analysis, and analyses of development and growth; and experimental investigations of call functions.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.042
GPT teacher head0.323
Teacher spread0.281 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBehaviourSame topicAnimal Vocal Communication and BehaviorFrench-language works237,207