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Record W4366808857 · doi:10.47886/9781934874707.ch7

Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management

2023· book-chapter· en· W4366808857 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Fisheries Society eBooks · 2023
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsFisheryEndangered speciesFishingEsoxGeographyWildlifeThreatened speciesInvasive speciesRecreational fishingIntroduced speciesPikeEcologyFish <Actinopterygii>BiologyHabitat

Abstract

fetched live from OpenAlex

Abstract.—Manitoba and Saskatchewan have diverse fish faunas: Manitoba has 86 species, 11 of which are nonnative, and Saskatchewan has 66 species, 8 of which are nonnative. The Committee on the Status of Endangered Wildlife in Canada has assessed eight species found in either Manitoba or Saskatchewan as endangered, threatened, or special concern. Aquatic invasive species control and education is a priority for both provinces with the recent proliferation of zebra mussel Dreissena polymorpha in Manitoba and Prussian Carp Carassisus gibelio in Saskatchewan. Recreational fishing is an important leisure activity and an economic driver in both provinces. The 2010 and 2015 Surveys of Recreational Fishing in Canada found that thousands of resident and nonresident anglers caught millions of fish annually. The three most important species in both provinces were Walleye Sander vitreus, Northern Pike Esox lucius, and Yellow Perch Perca flavescens. Commercial fisheries in both Manitoba and Saskatchewan (including bait fisheries) are locally and regionally important and the majority of fishers are Indigenous peoples. Recent average commercial fishery landings and values in Manitoba were 11 million kg worth Can$22.7 million and in Saskatchewan 2.7 million kg worth $4.9 million.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0050.004
Scholarly communication0.0050.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.021
GPT teacher head0.180
Teacher spread0.159 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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