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Record W4410108274 · doi:10.1139/cjes-2024-0137

The sabre-toothed cat <i>Smilodon fatalis</i> (Leidy, 1868) (Felidae, Machairodontinae) in the late Pleistocene–early Holocene of South America (Dolores Formation, Uruguay): new insights about its paleodistribution, taxonomy, and status of the genus

2025· article· en· W4410108274 on OpenAlexvenueno aff
Aldo Manzuetti, Washington Jones, Martı́n Ubilla, Daniel E. Perea, Andrés Rinderknecht

Bibliographic record

VenueCanadian Journal of Earth Sciences · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyPleistoceneHolocenePaleontologyTaxonomy (biology)GeochemistryZoologyBiology

Abstract

fetched live from OpenAlex

The sabre-toothed cat Smilodon fatalis (Leidy, 1868) was an iconic predator in the Americas during the Ice Age. While its distribution in North America is abundant, its record in South America is very scarce and is restricted to only a few locations. In the present contribution a new skull assigned to Smilodon fatalis is described. The specimen comes from the Dolores Formation (late Pleistocene–early Holocene, Lujanian Stage/Age) in southern Uruguay. This skull is elongated and narrow in its general shape; its nasals are not markedly high and, in the posterior part, the large lambdoid crest is anteroventrally straight, converging in the same plane with the mastoid process, characteristics observed in S. fatalis that clearly differentiate it from Smilodon populator Lund, 1842. Body mass estimations, according to allometric equations for extant felids, and the quantitative analyses (bivariate graphs) provide results consistent with the aforementioned taxonomic assignment. Based on this finding, which turns out to be, to date, the southernmost record for this species in the Americas, some paleobiogeographic and taxonomic implications in a regional context are discussed.

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 score0.672
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.022
GPT teacher head0.215
Teacher spread0.193 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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