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Record W2045952972 · doi:10.1139/cjfas-2014-0423

Retrospective weight-of-evidence analysis identifies substrate change as the apparent cause of recruitment failure in the upper Columbia River white sturgeon (<i>Acipenser transmontanus</i>)

2015· article· en· W2045952972 on OpenAlexaffvenue
D. Steven O. McAdam

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of EnvironmentUniversity of British Columbia
Fundersnot available
KeywordsSturgeonBiologyFish <Actinopterygii>EcologyFishery

Abstract

fetched live from OpenAlex

A weight-of-evidence evaluation of the potential cause of white sturgeon (Acipenser transmontanus) recruitment failure in the upper Columbia River evaluated 12 recruitment-failure hypotheses based on nine criteria. Recruitment-failure timing was estimated by identifying breakpoints in the hindcasted recruitment time series for three of four spatially distinct groups. Observed spatial and temporal recruitment decline patterns were then compared with expected patterns for each hypothesis (e.g., flow, temperature, turbidity, contaminants, and changes to the riverine fish community). The weight-of-evidence evaluation also considered criteria including coherence with theoretical, factual, and biological evidence and responses to impact reversal. Recruitment failure began about 1968, coincident with the initiation of upstream mainstem flow regulation. An 8- or 9-year lag in the recruitment decline of the Waneta group was particularly informative for hypothesis evaluations. The identification of increased fine substrates at spawning sites as the most plausible explanation for chronic recruitment failure has important implications regarding the apparent life stages affected and potential restoration approaches.

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)0.023
metaresearch head score (Gemma)0.082
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 score0.991
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.082
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.003
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.099
GPT teacher head0.267
Teacher spread0.168 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

Explore more

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