MétaCan
Menu
Back to cohort
Record W2889279371 · doi:10.1002/rra.3347

Effects of natal water dilution on the migration of Pacific salmon in a regulated river

2018· article· en· W2889279371 on OpenAlexafffund
Nolan N. Bett, Scott G. Hinch, Matthew T. Casselman

Bibliographic record

VenueRiver Research and Applications · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsBC Hydro (Canada)University of British Columbia
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaBC Hydro
KeywordsOncorhynchusSpawn (biology)DilutionHoming (biology)FisheryFish migrationEnvironmental sciencePopulationHydroelectricityFresh waterBiologyEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract Adult salmon are guided by chemical cues from their natal water during the spawning migration. Consequently, hydroelectric activities can have negative effects on adult salmon navigation if they alter the chemical properties of the homing environment. We conducted riverside experiments to examine the effects of natal water dilution resulting from hydroelectric operations on upstream navigation in a British Columbia river and the efficacy of current dilution management in providing clear migratory cues for two local populations of sockeye salmon ( Oncorhynchus nerka ) and a population of pink salmon ( Oncorhynchus gorbuscha ). We found that both sockeye populations exhibited behavioural preferences for pure natal water when compared with various levels of diluted natal water, whereas pink salmon, which spawn throughout the system and exhibit lower levels of natal site fidelity, appeared unaffected by natal water dilution. Our findings confirmed that current management operations were appropriate for ensuring timely migration to spawning grounds and highlight the potential effects of alterations to water odour properties on salmon migrations.

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 score0.495
Threshold uncertainty score0.415

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueRiver Research and ApplicationsSame topicFish Ecology and Management StudiesFrench-language works237,207