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Record W2140128724 · doi:10.1002/rra.2666

HYDRAULIC GEOMETRY AND LONGITUDINAL PATTERNS OF HABITAT QUANTITY AND QUALITY FOR RAINBOW TROUT (<i>Oncorhynchus mykiss</i>)

2013· article· en· W2140128724 on OpenAlexafffund
J. J. Laliberte, John R. Post, Jordan S. Rosenfeld

Bibliographic record

VenueRiver Research and Applications · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of EnvironmentUniversity of Calgary
FundersAlberta InnovatesJohnson and JohnsonAlberta Water Research Institute
KeywordsHabitatRainbow troutWatershedEnvironmental scienceHydrology (agriculture)Range (aeronautics)TroutEcologyGeologyFisheryBiologyGeotechnical engineeringFish <Actinopterygii>

Abstract

fetched live from OpenAlex

ABSTRACT We developed predictions of habitat quantity and quality for three life stages of rainbow trout, Oncorhynchus mykiss , across a range of stream sizes characterized by mean annual discharge of 1 to 50 m 3 s −1 . The physical habitat template was created by nesting a reach‐scale two‐dimensional hydrodynamic model (River2D) within a downstream hydraulic geometry system using published coefficients for low‐gradient and high‐gradient watersheds. This provided both longitudinal and transverse estimates of depth and velocity profiles that, when combined with habitat suitability curves for the life stages, resulted in predictions of habitat quantity (weighted usable area) and habitat quality (the proportion of the stream profile that provided useable habitat) for rainbow trout along the stream continuum. Habitat quantity increased asymptotically for all life history stages but increased more rapidly in the low‐gradient watershed. Habitat quality decreased non‐linearly for young‐of‐the‐year and peaked at intermediate stream sizes for juveniles in both low‐gradient and high‐gradient watersheds. Adult habitat quality peaked in the high‐gradient watershed but increased asymptotically in the low‐gradient watershed, presumably due to lower mean velocities at larger stream sizes. Incorporation of transverse variation in depth and velocity in our physical habitat template provides a more realistic representation of habitat quantity and quality than do earlier assessments based on simple modal estimates of depth and velocity. Copyright © 2013 John Wiley &amp; Sons, Ltd.

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 score0.005
Threshold uncertainty score0.312

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 studies0.0000.001
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 head0.061
GPT teacher head0.343
Teacher spread0.282 · 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

Citations11
Published2013
Admission routes2
Has abstractyes

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