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Aquatic versus terrestrial locomotion: comparative performance of two ecologically contrasting species of European natricine snakes

2007· article· en· W2120333162 on OpenAlexafffund
Leigh Anne Isaac, Patrick T. Gregory

Bibliographic record

VenueJournal of Zoology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Kent
KeywordsNatrixBiologyEcologyInterspecific competitionRange (aeronautics)

Abstract

fetched live from OpenAlex

Abstract One indicator of ecological differences between species is differential locomotory performance in different media. In this study, we compare locomotory speed of two species of natricine snakes on land and in water at a range of temperatures in the laboratory. As expected, both species moved more quickly at higher temperatures and both swam faster than they moved on land. However, the difference between aquatic and terrestrial speed was much greater for the aquatic species, Natrix maura, than for the semi‐aquatic Natrix natrix. Furthermore, although N. maura was significantly faster than N. natrix in water, the opposite was true on land. In fact, N. maura was reluctant to move very far on land at all and would not complete a 2 m terrestrial course. Although we found no evidence of a negative correlation between the aquatic and terrestrial speeds of individual N. natrix, this interspecific comparison is consistent with the notion of a tradeoff between performance abilities on land and in water.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.041
GPT teacher head0.278
Teacher spread0.238 · 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

Citations16
Published2007
Admission routes2
Has abstractyes

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