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Record W6995858288

Predicting coastal cutthroat trout molt productive capacity from physiographic variables

2016· other· en· W6995858288 on OpenAlexfundno aff

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2016
Typeother
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersCalifornia Department of Fish and WildlifeFisheries and Oceans CanadaMinistry of EnvironmentWashington Department of Fish and WildlifeRoyal Roads UniversityMinistry of Forests, Lands and Natural Resource OperationsWashington State University
KeywordsFish migrationAbundance (ecology)HabitatTroutWatershedAlewifeChannel (broadcasting)Oncorhynchus
DOInot available

Abstract

fetched live from OpenAlex

For the management of anadromous coastal cutthroat trout, fisheries managers require an understanding of how physiographic variables, at a watershed scale, influence cutthroat smolt productive capacity. The primary purpose was to produce a practical desktop procedure to reliably predict smolt abundance based upon physiographic variables. A total of 653 annual
\nestimates of smolt abundance from 50 watersheds in British Columbia and Washington State were assessed in this study. Cutthroat dominated reaches were identified using hydrology and mapping data, then modelled to predict smolt abundance. The model results found primarily that smolt abundance was weakly correlated with permanent stream length of 0-4% channel gradient
\nand lake area of 0-5 ha. The results suggest that smolt abundance is limited partially by the availability of physical habitat within a watershed. The model performance could have been influenced by uncertainties related to the species life history diversity, identification, and undocumented barriers to fish movement.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.609
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.000

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 head0.005
GPT teacher head0.158
Teacher spread0.154 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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