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Environmental and lunar cues are predictive of the timing of river entry and spawning‐site arrival in lake sturgeon <i>Acipenser fulvescens</i>

2012· article· en· W2050611979 on OpenAlexaff
Patrick S. Forsythe, Kim T. Scribner, James A. Crossman, Ashok Ragavendran, Edward A. Baker, Courtney L. Davis, Kelly Smith

Bibliographic record

VenueJournal of Fish Biology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsGovernment of British ColumbiaMinistry of ForestsBC Hydro (Canada)
Fundersnot available
KeywordsLake sturgeonAcipenserHabitatBiologyFisheryFull moonEcologyPopulationFish <Actinopterygii>SturgeonEnvironmental scienceDemography

Abstract

fetched live from OpenAlex

The associations were quantified between daily and interannual variation in the timing of a closed population of lake sturgeon Acipenser fulvescens migration and arrival at spawning sites with stream environmental and lunar covariates. Spawning data were gathered from 1262 fish in Black Lake, Michigan 2001 to 2008 and by video monitoring 2000 to 2002. Sex-specific variation in responses to external cues was also tested. Results showed that a greater number of individuals initiated migration from lake to riverine habitats at dawn and dusk relative to other times of the day. Current and lagged effects of water temperature and river discharge, and periods in the lunar cycle were important variables in models quantifying movements into the river and timing of adult arrival at spawning sites. Different suites of covariates were predictive of A. fulverscens responses during different periods of the spawning season. The timing of initiation of migration and spawning, and the importance of covariates to the timing of these events, did not differ between sexes. Stream flow and temperature covaried with other variables including day length and the lunar cycle. Anthropogenic disruption of relationships among variables may mean that environmental cues may no longer reliably convey information for Acipenseriformes and other migratory fishes.

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 score0.003
Threshold uncertainty score0.254

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations94
Published2012
Admission routes1
Has abstractyes

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