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Record W1572919179 · doi:10.1080/14634988.2014.936804

Fish species composition, distribution and abundance trends in the open-coastal waters of northeastern Lake Ontario, 1992–2012

2015· article· en· W1572919179 on OpenAlexaffabout
James A. Hoyle

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsHatch (Canada)Ministry of Natural Resources and Forestry
Fundersnot available
KeywordsFisheryAbundance (ecology)TransectAlewifeBenthic zoneTroutPerchEcologyGeographyBass (fish)Environmental scienceBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Bottom set gill nets were used to describe and track the fish community in northeastern Lake Ontario from 1992–2012. Six fixed, depth-stratified transects, spread more or less evenly from the mouth of the St. Lawrence River in the Kingston Basin to Brighton in central Lake Ontario, were sampled annually during summer. The balanced sampling design provided a broad picture of the warm, cool and coldwater fish community inhabiting open-coastal waters out to about 30 m water depth. Catch results were summarized by geographic area and depth strata to describe species distribution patterns, and presented graphically to illustrate annual abundance trends of the most important fish species (Alewife, Lake Trout, Yellow Perch, Walleye, Round Goby, Lake Whitefish, Brown Trout, Rock Bass, Smallmouth Bass, Chinook Salmon, Burbot, Cisco and Round Whitefish). Many of these dominant species showed peak abundance levels in the early 1990 s followed by decline. Of particular note, members of the coldwater benthic-oriented species assemblage, having all declined dramatically in the 1990s, remain at very low abundance levels, and their future prognosis appears bleak.

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 score0.920
Threshold uncertainty score0.993

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 studies0.0000.000
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 head0.025
GPT teacher head0.249
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

Citations15
Published2015
Admission routes2
Has abstractyes

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