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Record W1590498581 · doi:10.1080/14634988.2012.734691

Effect of aquatic macrophyte cover and fetch on spatial variability in the biomass and growth of littoral fishes in bays of Prince Edward County, Lake Ontario

2012· article· en· W1590498581 on OpenAlexaffabout
Robert G. Randall, C. M. Brousseau, James A. Hoyle

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of Natural Resources and ForestryHatch (Canada)Fisheries and Oceans Canada
Fundersnot available
KeywordsMacrophyteBiomass (ecology)Environmental scienceGonadosomatic IndexLittoral zoneBayFisheryLepomisAquatic plantAbundance (ecology)FetchPerchEcologyDreissenaOceanographyBiologyBivalviaFish <Actinopterygii>Population

Abstract

fetched live from OpenAlex

Biomass and growth of Pumpkinseed ( Lepomis gibbosus), Yellow Perch ( Perca flavescens) and cohabiting species varied among different bays of Prince Edward County in eastern Lake Ontario. Biomass (B) and a calculated fish production (P) index of fishes, estimated as the product of average seasonal biomass and P/B, was about 6x higher at the electrofishing transects with medium to high macrophyte cover and low fetch than at sites where macrophytes were absent or sparse and fetch was high. The production index -aquatic plant relationship was species-dependent. The biomass component of fish production was related to macrophyte cover but the growth component and P/B (determined by allometry with body size) was not. Biomass and inferred production could be predicted from fetch, macrophyte abundance and water temperature, but with low precision. Results were consistent with index trawling in showing that changes in aquatic vegetation following Dreissena colonization in the Bay of Quinte and vicinity have affected the abundance of phytophilic fishes in this region of eastern Lake Ontario.

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)0.006
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 score0.381
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.231
Teacher spread0.224 · 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

Citations8
Published2012
Admission routes2
Has abstractyes

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