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Record W4286906380 · doi:10.4000/147ox

Évaluer le degré de mobilité des rennes paléolithiques à partir de l’étude ostéométrique de métacarpiens

2021· preprint· fr· W4286906380 on OpenAlexaff
Ana Belén Galán López, Sandrine Costamagno, Ariane Burke

Bibliographic record

Venuenot available
Typepreprint
Languagefr
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Le Renne (Rangifer tarandus) a joué un rôle important pour les populations humaines en Europe occidentale et centrale durant une grande partie de la période paléolithique. Dans le Sud-Ouest de la France (notamment au Magdalénien), le Renne figure fréquemment parmi les proies privilégiées des groupes de chasseurs-cueilleurs. Malgré les nombreuses tentatives de reconstitution du comportement migratoire des rennes paléolithiques, il n’existe pas de consensus quant à son degré de mobilité. Les données éthologiques modernes indiquent que les troupeaux de rennes adoptent différentes stratégies de mobilité en fonction du type d’habitat et de la topographie. Le projet Emorph vise à rechercher des critères morphométriques, en combinaison avec des techniques d´apprentissage automatique, permettant d’identifier l’étendue des migrations du Renne. Fondés sur l’étude de populations de caribous modernes aux comportements migratoires distincts, les résultats obtenus seront ensuite appliqués sur divers assemblages magdaléniens du Sud-Ouest de la France pour reconstruire la mobilité de ces rennes tardiglaciaires.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.315
Teacher spread0.274 · 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
Published2021
Admission routes1
Has abstractyes

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