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Record W4308726173 · doi:10.22621/cfn.v136i2.2853

Dispelling myths about the origins of wolf–coyote hybrids and related <i>Canis</i> species in Ontario

2022· article· en· W4308726173 on OpenAlexafffundvenueabout
Tyler J. Wheeldon, Brent R. Patterson

Bibliographic record

VenueThe Canadian Field-Naturalist · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMinistry of Natural Resources and Forestry
FundersOntario Ministry of Natural Resources and ForestryMinistry of Natural Resources
KeywordsCanisHybridMythologyBiologyZoologyEcologyGeographyArtBotany

Abstract

fetched live from OpenAlex

Long-standing myths exist about the origins of wolf–coyote hybrids and related Canis species in Ontario. Specifically, there is a perceived controversy whether they are the product of natural hybridization that occurred between wolves and coyotes in the wild during the last century or the descendants of animals that escaped or were released from captive colonies or controlled breeding experiments. We review the relevant evidence and conclude that captive colonies and controlled breeding experiments were unlikely to have played any role in the origins of wolf–coyote hybrids and related Canis species in 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 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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.015
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.192
Teacher spread0.182 · 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

Citations1
Published2022
Admission routes4
Has abstractyes

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