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Record W4389184481 · doi:10.5869/fc.2023.v28-1.63

Recent Range Records of Crayfish (Faxonius) From Far North Ontario, Canada

2023· article· en· W4389184481 on OpenAlexaffabout
Jane Devlin, David Beresford

Bibliographic record

VenueFreshwater Crayfish · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsTrent UniversityMinistry of Natural Resources and Forestry
Fundersnot available
KeywordsCrayfishGeographyWetlandWildlifeBayBorealRange (aeronautics)FisheryWilderness areaWildlife managementBaseline (sea)EcologyWildernessArchaeologyBiology

Abstract

fetched live from OpenAlex

Abstract The Far North region of Ontario, Canada, is a wilderness area comprising 451,808 square kilometers of intact boreal forest and wetlands. Until the work presented here the most recent extensive survey for crayfish in the Far North region of Ontario was conducted in 1963. We sampled 81 locations across the Far North region of northern Ontario and the Hudson Bay Lowlands, Canada from 2009 to 2014 catching 96 specimens from 2 species: Faxonius virilis (Hagen) (92) and Faxonius propinquus (Girard) (4) These records define the northern boundary of freshwater crayfish. Unlike much of North America, species records were consistent with surveys conducted over 55 years ago. The Far North region of Ontario is facing proposed development at a scale not seen before, and our records provide necessary baseline data for ecosystem and wildlife monitoring, understanding potential future impacts, and to support sustainable resource management.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.193
Teacher spread0.178 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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