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Record W4402885060 · doi:10.1139/as-2024-0021

A method for long-term year-round water temperature monitoring in salmonid spawning habitats in remote dynamic streams

2024· article· en· W4402885060 on OpenAlexafffundvenue
Karen M. Dunmall, Brian Cabral, Neil J. Mochnacz, James D. Reist

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersW. Garfield Weston FoundationGwich'in Renewable Resources BoardAmerican Fisheries SocietyNatural Sciences and Engineering Research Council of CanadaFisheries Joint Management Committee
KeywordsSTREAMSTerm (time)HabitatEnvironmental scienceFisheryEcologyHydrology (agriculture)Remote sensingGeographyBiologyGeologyComputer sciencePhysicsGeotechnical engineering

Abstract

fetched live from OpenAlex

Warming temperatures have added urgency to characterizing the thermalscapes and thermal tolerances of cold-adapted fishes to effectively manage and conserve such species. This is especially relevant at high latitude and high elevation streams, which are experiencing rapid environmental changes, yet are data-poor, remote, and difficult to access. Here, we describe a method to assess temporal and spatial variation in surface and hyporheic water temperatures that can be effectively deployed to remain year-round in remote dynamic streams. We then demonstrate the utility of this method by assessing Dolly Varden Salvelinus malma spawning sites in a remote river. Characterizing and quantifying the amount of viable thermal habitat for cold-adapted species improves predictions of how warming may affect high latitude and high elevation stream ecosystems. Together with species-specific thermal tolerances, this information can then be used to identify the thermal refugia that are essential for conservation of endemic species, and assess risks associated with range expansions of potentially colonizing species.

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)0.001
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 score0.019
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.304
Teacher spread0.292 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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