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REDUCTIONS IN PROLONGED SWIMMING CAPACITY FOLLOWING FRESHWATER COLONIZATION IN MULTIPLE THREESPINE STICKLEBACK POPULATIONS

2011· article· en· W1766560726 on OpenAlexaff
Anne C. Dalziel, Tim Vines, Patricia M. Schulte

Bibliographic record

VenueEvolution · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSticklebackBiologyColonizationGasterosteusEcologyZoologyFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We compared ancestral anadromous-marine and nonmigratory, stream-resident threespine stickleback (Gasterosteus aculeatus) populations to examine the outcome of relaxed selection on prolonged swimming performance. We reared marine and stream-resident fish from two locations in a common environment and found that both stream-resident populations had lower critical swimming speeds (U(crits) ) than marine populations. F1 hybrids from the two locations displayed significant differences in dominance, suggesting that the genetic basis for variation in U(crit) differs between locations. To determine which traits evolved in conjunction with, and may underlie, differences in performance capacity we measured a suite of traits known to affect prolonged swimming performance in fish. Although some candidate traits did not evolve (standard metabolic rate and two body shape traits), multiple morphological (pectoral fin size, shape, and four body shape measures) and physiological (maximum metabolic rate; MMR) traits evolved in the predicted direction in both stream-resident populations. However, data from F1 hybrids suggested that only one of these traits (MMR) had dominance effects similar to those of U(crit) in both locations. Overall, our data suggest that reductions in prolonged swimming performance were selected for in nonmigratory populations of threespine stickleback, and that decreases in MMR may mediate these reductions in 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.708

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 head0.049
GPT teacher head0.227
Teacher spread0.178 · 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

Citations65
Published2011
Admission routes1
Has abstractyes

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