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Record W2290989984 · doi:10.1111/jfb.12925

Use of streambed substrate as refuge by steelhead or rainbow trout <i>Oncorhynchus mykiss</i> during simulated freshets

2016· article· en· W2290989984 on OpenAlexaff
F. K. Ligon, Rodney J. Nakamoto, Bret C. Harvey, Peter F. Baker

Bibliographic record

VenueJournal of Fish Biology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsStillwater (Canada)
Fundersnot available
KeywordsRainbow troutFlumeFish measurementSubstrate (aquarium)FisheryBiologyEnvironmental scienceEcologyFish <Actinopterygii>MathematicsFlow (mathematics)

Abstract

fetched live from OpenAlex

A flume was used to estimate the carrying capacity of streambed substrates for juvenile steelhead or rainbow trout Oncorhynchus mykiss seeking refuge from simulated freshets. The simulated freshets had mean water column velocities of c. 1·1 m s(-1). The number of O. mykiss finding cover within the interstices of the substrate was documented for different substrate sizes and levels of embeddedness. The availability of suitable refuges determined the carrying capacity of the substrate for O. mykiss. For the size of the O. mykiss tested [mean ± s.d. fork length (L(F)) = 122 ± 12.6 mm], the number of interstices with depths ≥200 mm measured with a 14.0 mm diameter flexible plastic tube was the best predictor of the number of O. mykiss able to find cover (r(2) = 0.75). Oncorhynchus mykiss seeking refuge from freshets may need deeper interstices than those seeking concealment at autumn or winter base flows. The availability of interstices suitable as refuge from high flows may determine autumn and winter carrying capacity.

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 score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.249
Teacher spread0.231 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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