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Effects of lunar cycles on the activity patterns and depth use of a temperate sport fish, the largemouth bass, <i>Micropterus salmoides</i>

2008· article· en· W2139677169 on OpenAlexafffund
K. C. Hanson, S. ARROSA, Caleb T. Hasler, Cory D. Suski, David P. Philipp, G.H. Niezgoda, Steven J. Cooke

Bibliographic record

VenueFisheries Management and Ecology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Illinois at Urbana-ChampaignU.S. Fish and Wildlife ServiceMinistry of Natural ResourcesIllinois Department of Natural Resources
KeywordsMicropterusBass (fish)Full moonFisheryFish <Actinopterygii>FishingTemperate climateCatch and releaseBiologyEnvironmental scienceEcologyRecreational fishing

Abstract

fetched live from OpenAlex

Abstract The behaviour of free‐swimming, telemetered, adult largemouth bass, Micropterus salmoides (L.), was monitored using a whole lake, three‐dimensional acoustic telemetry array to test the hypothesis that fish activity and depth distribution are influenced by lunar phase. The percent of lunar face shining and whether the moon was waxing or waning were significant determinants of swimming activity and depth distribution during most of the lunar cycles evaluated, although these patterns were not consistent across the year. In spring and summer, daily depth distribution followed a pattern in which the fish inhabited greater depths on the 26–50% and 51–75% waxing moon. Fish daily movement distances were five times greater during spring and summer than in winter, but no repeatable patterns were noted in relation to lunar periodicity. This research suggests that solunar tables frequently consulted by recreational anglers may have little predictive value for identifying peak fishing time.

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 score0.019
Threshold uncertainty score0.408

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 science0.0000.001
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 head0.012
GPT teacher head0.179
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 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

Citations20
Published2008
Admission routes2
Has abstractyes

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