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Record W2277420509 · doi:10.1139/cjfas-2015-0293

Are brown trout replacing or displacing bull trout populations in a changing climate?

2016· article· en· W2277420509 on OpenAlexvenueno aff
Robert Al‐Chokhachy, David A. Schmetterling, Chris Clancy, Pat Saffel, Ryan P. Kovach, Leslie G. Nyce, Brad Liermann, Wade Fredenberg, Ron Pierce

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBrown troutTroutSalmoOverwinteringForagingBiologyAbundance (ecology)FisheryPopulationEcologySalvelinusHabitatFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Understanding how climate change may facilitate species turnover is an important step in identifying potential conservation strategies. We used data from 33 sites in western Montana to quantify climate associations with native bull trout (Salvelinus confluentus) and non-native brown trout (Salmo trutta) abundance and population growth rates (λ). We estimated λ using exponential growth state-space models and delineated study sites based on bull trout use for either spawning and rearing (SR) or foraging, migrating, and overwintering (FMO) habitat. Bull trout abundance was negatively associated with mean August stream temperatures within SR habitat (r = −0.75). Brown trout abundance was generally highest at temperatures between 12 and 14 °C. We found bull trout λ were generally stable at sites with mean August temperature below 10 °C but significantly decreasing, rare, or extirpated at 58% of the sites with temperatures exceeding 10 °C. Brown trout λ were highest in SR and sites with temperatures exceeding 12 °C. Declining bull trout λ at sites where brown trout were absent suggest brown trout are likely replacing bull trout in a warming climate.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.961
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.037
GPT teacher head0.244
Teacher spread0.206 · 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

Citations59
Published2016
Admission routes1
Has abstractyes

Explore more

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