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Record W2005239169 · doi:10.1139/cjfas-2013-0115

Introduced lake trout exhibit life history and morphological divergence with depth

2013· article· en· W2005239169 on OpenAlexvenueno aff
Craig P. Stafford, Megan V. McPhee, Lisa A. Eby, Fred W. Allendorf

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersPennsylvania Game Commission
KeywordsSalvelinusTroutFlatheadBiologyPredationEcologyPopulationFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We found that an introduced population of lake trout (Salvelinus namaycush) in Flathead Lake, Montana, USA, exhibited divergent life history, diet, and morphology after the invasion of Mysis diluviana. A correspondence between stable isotopes (δ13C and δ15N) in lake trout muscle and their prey suggests that individual lake trout exhibited depth preferences. Lake trout 451–600 mm total length showed morphological distinctness between shallow (0–25 m) and deep (60–100 m) collections wherein the latter had deeper bodies and larger eyes. Furthermore, these deep lake trout fed more heavily on Mysis, grew slower, and matured at a smaller size. Lack of genetic divergence between depth groups and the rapid divergence of life histories after Mysis invasion suggest a strong role for environment in producing the observed ecotypic variation. Our research supports resource partitioning by depth and diet as a drivers of phenotypic diversity in lake trout, providing insights into the origins of morphotypes and guidance for conservation of native populations.

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)0.000
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.181
Teacher spread0.162 · 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

Citations30
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→