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Record W4365397779 · doi:10.7202/1098171ar

Le modelé d’érosion glaciaire de l’île d’Anticosti révélé par l’imagerie LiDAR

2023· article· fr· W4365397779 on OpenAlexaffvenueabout
Bernard Hétu, Pascal Bernatchez, Jérôme Dubé

Bibliographic record

VenueLe Naturaliste canadien · 2023
Typearticle
Languagefr
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsGeology

Abstract

fetched live from OpenAlex

Des images LiDAR à haute résolution dévoilent des modelés d’érosion glaciaire inédits qui nuancent fortement les interprétations publiées antérieurement concernant l’histoire glaciaire de l’île d’Anticosti. Sculptées dans le roc, ces formes d’érosion glaciaire sont très diversifiées : lacs de surcreusement glaciaire, drumlins rocheux, cannelures géantes, mégaqueues-de-rat, dalles de roc déplacées par les glaciers. Bien que pour la plupart discrètes, ces formes d’érosion glaciaire sont présentes partout sur l’île. Toutefois, c’est dans les basses terres de l’est et de l’ouest que l’empreinte glaciaire est la plus forte. Dans les basses terres de l’est, l’écoulement glaciaire vers le sud-ouest, observé partout sur l’île, a été suivi par un écoulement vers le sud-est. Ces 2 écoulements avaient leur source sur le Bouclier canadien (inlandsis laurentidien). Un écoulement tardif vers le nord a été observé dans la moitié nord du plateau central (au nord de la rivière Jupiter). Cet écoulement vers le nord est attribué à la calotte glaciaire régionale qui a occupé le plateau central durant la déglaciation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.023
GPT teacher head0.240
Teacher spread0.217 · 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 routes3
Has abstractyes

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