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Record W1980959426 · doi:10.1139/z01-226

Fast-start swimming performance and reduction in lateral plate number in threespine stickleback

2002· article· en· W1980959426 on OpenAlexvenueno aff
Carolyn Bergstrom

Bibliographic record

VenueCanadian Journal of Zoology · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSticklebackGasterosteusBiologyPredationAbiotic componentEscape responsePredatorEcologyFish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

Threespine stickleback (Gasterosteus aculeatus) have colonized freshwater habitats in circumboreal coastal regions, resulting in populations with variable but generally reduced lateral plate numbers compared with marine ancestors. Several abiotic and ecological factors associated with variation in lateral plate number among freshwater populations of G. aculeatus have been found, including large-scale climatic effects, variation in water-flow rates and levels of dissolved calcium, and the presence or absence of predatory fish. In addition, it has been proposed that plate reduction might be an adaptation for evading predator pursuit that enhances fast-start performance. If this hypothesis is correct, one would predict that fast-start performance would improve as lateral plate numbers decrease. I tested this prediction by comparing fast-start performance among stickleback with different numbers of lateral plates within two freshwater populations. Fast-starts of individual stickleback were video-recorded at 60 Hz and maximum velocity, maximum acceleration, displacement, and body curvature were calculated for each fish. Lateral plate number was significantly negatively correlated with velocity and displacement but not with acceleration or curvature. These results suggest that reduction in lateral plate number has the potential to be advantageous in some predation regimes because of its association with enhanced fast-start performance.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.189
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 teacher head, not a consensus.

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

Citations132
Published2002
Admission routes1
Has abstractyes

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