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Record W2186886215 · doi:10.1093/czoolo/60.5.571

Time to feed: How diet, competition, and experience may influence feeding behaviour and cannibalism in wood frog tadpoles Lithobates sylvaticus

2014· article· en· W2186886215 on OpenAlexaff
Dale M. Jefferson, Keith A. Hobson, Douglas P. Chivers

Bibliographic record

VenueCurrent Zoology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsCannibalismBiologyLithobatesPredationCompetition (biology)EcologyZoologyCrayfishAmphibian

Abstract

fetched live from OpenAlex

Abstract Wood frog Lithobates sylvaticus tadpoles develop in temporary wetlands where high population densities can force tadpoles into aggregations that intensify competition and can lead to cannibalism. However, chemical alarm cues released from injured conspecifics could also dissuade cannibalism. The purpose of this study was to test mechanisms that may influence cannibalistic behaviour. We tested whether the tendency of tadpoles to consume conspecifics would increase with the presence of competition and/or cues of profitable diets. Tadpoles placed in 1L experimental containers were tested for feeding initiation times of multiple diets, including conspecific tissues and conspecific tissues combined with chemical cues from the alternative diets (brine shrimp and cornmeal). Tadpoles were tested in the presence and absence of a competitor, and at multiple times over the course of the study. Tadpoles exhibited an altered response to diets over time; however the presence of a competitor reduced response times to all diets including conspecific tissues. Similarly, the presence of specific diets also reduced the response time of tadpoles to conspecific tissues. These results suggest competition among feeding tadpoles could result in aggressive behaviour leading to indiscriminate predation and cannibalism.

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 score0.013
Threshold uncertainty score0.562

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 studies0.0000.001
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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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