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Record W2977303494 · doi:10.1111/eth.12956

The cost of aggression in an animal without weapons

2019· article· en· W2977303494 on OpenAlexaff
Xiaomeng Guo, Reuven Dukas

Bibliographic record

VenueEthology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAggressionSurvivorship curvePoison controlPsychologyBiologySocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Abstract To understand the prevalence and conditional use of aggression among animals, one has to know its costs and benefits. The obvious cost of aggression in animals that possess teeth, claws or other specialized weaponry is injury. Many species, however, do not have such body parts and thus cannot readily injure others. The cost of aggression in these animals is not well studied. We tested whether aggression has a fitness cost in fruit flies, which can serve as a model species for animals without weapons that engage in aggression. In three experiments employing distinct protocols, we allowed focal flies to fight for control of an attractive food patch over 4 days and then compared their survivorship to that of flies not engaged in conflict. In all three experiments, fly survivorship was lower in the aggression than no‐aggression treatments. Microscopic examination revealed no differences in wing damage between flies of the aggression and no‐aggression treatments. The two most likely, non‐mutually exclusive explanations for lower survivorship post‐fighting are physiological changes due to stress, and metabolic alterations associated with a life‐history strategy optimized for high‐conflict settings.

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 score0.105
Threshold uncertainty score0.108

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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

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