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Record W2976044176 · doi:10.1080/14634988.2019.1672463

Historical changes in the fish communities of the Credit River watershed

2019· article· en· W2976044176 on OpenAlexaffabout
Brett Allen, Nicholas E. Mandrak

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpecies richnessWatershedEcologyGeographyUrbanizationFisheryEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Watersheds in southern Ontario are of high conservation concern due to their diverse fish communities, productive environments, and threats from numerous anthropogenic stressors. The Credit River watershed, located west of the Greater Toronto Area, has over 60 fish species, and multiple stressors including urbanization, climate change, and aquatic invasive species. This study examines fish community change in the Credit River watershed. Historical fish datasets collected in the watershed from 1954 to 2015 were analyzed to examine richness patterns, temporal trends in species distributions, and faunal similarity at the site and sub-watershed levels. Species richness increased over time at the site and sub-watershed level, displaying predictable richness patterns due to anthropogenic introductions and changes in sampling methods. Species distribution patterns remained largely stable over time with decreases in some species (e.g. Redside Dace) and increases in others (e.g. Largemouth Bass). Faunal similarity also increased over time at the site and sub-watershed level, indicating that the fish communities in the Credit River are homogenizing.

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 score0.152
Threshold uncertainty score0.892

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 science0.0010.001
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 head0.019
GPT teacher head0.225
Teacher spread0.206 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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