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Record W4362450028 · doi:10.1002/jqs.3516

Dire wolf (<i>Canis dirus</i>) from the late Pleistocene of southern Canada (Medicine Hat, Alberta)

2023· article· en· W4362450028 on OpenAlexafffundabout
Ashley R. Reynolds, Talia M. Lowi‐Merri, Alexandria L. Brannick, Kevin L. Seymour, C. S. Churcher, David C. Evans

Bibliographic record

VenueJournal of Quaternary Science · 2023
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPleistoceneCanisGeographyMorphometricsTaxonRange (aeronautics)ArchaeologyBiogeographyEcologyZoologyBiology

Abstract

fetched live from OpenAlex

ABSTRACT The dire wolf ( Canis dirus ) had a broad geographic range in Pleistocene North and South America. Its northernmost occurrence has been reported from late Pleistocene deposits in Medicine Hat, Alberta, representing the only record of the taxon in Canada. However, the dentary upon which these reports were based has never been described or illustrated. The Medicine Hat specimen is badly crushed and appears to be from an old individual, which precludes the observation of adult diagnostic morphological characters. Geometric morphometrics were used to test the previous identification of the Medicine Hat dentary. A landmark‐based principal component analysis and a canonical variates analysis suggests that the specimen more strongly resembles dire wolf specimens than grey wolf ( Canis lupus ). Identification of the Medicine Hat specimen as C. dirus supports it as the northernmost occurrence of this species in North America. However, we note the potential for allometric relationships that may confound differentiation between grey and dire wolves based on the morphology of the dentary. This study concludes by identifying future work needed in the areas of canid allometry and the biogeography of late Pleistocene North America and Beringia.

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)0.001
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 score0.322
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.283
Teacher spread0.242 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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