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Frequency, composition and stability of associations among individual largemouth bass (<i>Micropterus salmoides</i>) at diel, daily and seasonal scales

2007· article· en· W2153291513 on OpenAlexafffundabout
Caleb T. Hasler, K. C. Hanson, Steven J. Cooke, Rowland R. Tinline, Cory D. Suski, G.H. Niezgoda, Frank J. S. Phelan, David P. Philipp

Bibliographic record

VenueEcology Of Freshwater Fish · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton UniversityQueen's University
FundersCampus Research BoardNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaKillam Trusts
KeywordsMicropterusBass (fish)Diel vertical migrationBiologyFisheryEcologyLepomis macrochirusFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract – A whole‐lake acoustic telemetry observatory situated in eastern Ontario was used to continuously monitor the three‐dimensional position of 20 largemouth bass ( Micropterus salmoides ) over a 120‐h period during the winter and a separate 120‐h period during the early spring. These data were used to evaluate the frequency and stability of associations among fish to provide an understanding of seasonal aggregations and the sociobiology of largemouth bass. The temporal and spatial proximity of each fish relative to the other 19 individuals was assessed and, based on our definition of spatial/temporal proximity (i.e., two fish having an average hourly position &lt;2 m apart), associations were shown to vary among fish, as well as diurnally, daily and seasonally. Associations during the winter were found to be more stable and involved fewer fish than associations during the spring. Of those fish that formed pair aggregations during the winter and spring study periods, male–female pairs occurred more often than male–male and female–female pairs. Our analysis also demonstrated that associations occurred primarily during daylight hours, suggesting that fish may use visual cues to form these aggregations.

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)0.001
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 score0.128
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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

Citations13
Published2007
Admission routes3
Has abstractyes

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