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Record W2750881896 · doi:10.1670/17-058

Performance and Movement in Relation to Postmetamorphic Body Size in a Pond-Breeding Amphibian

2017· article· en· W2750881896 on OpenAlexaff
Katharine T. Yagi, David M. Green

Bibliographic record

VenueJournal of Herpetology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiological dispersalBiologyAmphibianJumpingEcologyMovement (music)Mark and recaptureUncorrelatedPopulationDemographyStatisticsMathematics

Abstract

fetched live from OpenAlex

We examined the effect of body size on locomotor performance and movement behavior in early postmetamorphic toadlets of Fowler's Toad (Anaxyrus fowleri). Dispersal, if strictly density dependent, should be favored among the relatively small toadlets that emerge from crowded growth conditions, but not among the relatively large toadlets that result from low-density conditions; however, smaller toadlets may have less physical capability to disperse than larger toadlets. The net result may thus be that actual dispersal probability is greatest among intermediate-sized toadlets. Using toadlets of various sizes purposefully raised by manipulating their densities as tadpoles, we tested toadlet locomotor jumping endurance in relation to body size. We also compared the animals' initial body size against their overall movements in the wild, determined using capture–recapture methods, over 2 yr as they grew from toadlets to adults. We calculated movement rate and dispersal probability for each individual recaptured more than twice and determined whether successful movement strategies were correlated or uncorrelated. Our results show that toadlets of intermediate size are most likely to disperse farthest, even though they do not necessarily exhibit the highest levels of endurance. Therefore, knowledge of individual life experience across multiple life stages may be necessary to understand dispersal tendencies in amphibians and may be required in future studies aiming to predict dispersal and population dynamics.

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)0.001
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 score0.031
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.243
Teacher spread0.231 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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