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Record W3081927424 · doi:10.1163/1568539x-bja10027

No evidence that songbirds use odour cues to avoid malaria-infected conspecifics

2020· article· en· W3081927424 on OpenAlexaff
Leanne A. Grieves, Elizabeth A. MacDougall‐Shackleton

Bibliographic record

VenueBehaviour · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBird parasitology and diseases
Canadian institutionsWestern University
Fundersnot available
KeywordsBiologyZoologyMalariaEcologyOlfactory cuesOlfactionImmunology

Abstract

fetched live from OpenAlex

Abstract Many animals have evolved mechanisms to detect and avoid parasitized conspecifics, primarily through odour cues, but whether birds are capable of odour-mediated parasite avoidance is unknown. Recently, we showed that exposing song sparrows (Melospiza melodia) to avian malaria parasites (Plasmodium sp.) alters the chemical composition of their preen oil, which is the major source of body odour in birds. Here, we presented song sparrows with preen oil from uninfected (sham-inoculated) and malaria-infected conspecifics, predicting that birds would spend more time with odour cues from uninfected than infected birds. Birds without detectable malarial infections spent about 50% more time with preen oil from uninfected than infected conspecifics, and females spent nearly twice as much time with preen oil from uninfected than infected conspecifics. However, neither difference was statistically significant. Song sparrows may be able to detect odour cues of infection, but further experiments are needed to confirm or refute this.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.000

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 head0.124
GPT teacher head0.303
Teacher spread0.179 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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