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Record W4401792696 · doi:10.1144/jgs2024-057

Aeromagnetic data reveals buried Quaternary drainage patterns in the Gulf of St Lawrence (Canada)

2024· article· en· W4401792696 on OpenAlexaffabout
Nicolas Pinet, V Brake

Bibliographic record

VenueJournal of the Geological Society · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsQuaternaryGeologyDrainagePaleontologyOceanographyGeochemistry

Abstract

fetched live from OpenAlex

Short-wavelength, low-amplitude magnetic anomalies form dendritic patterns on the shallow water (<100 m depth) Magdalen Plateau in the Gulf of St Lawrence, Canada. High-resolution seismic data indicate that these magnetic anomalies are associated with 0.6–2 km wide incised Quaternary valleys with relatively flat bottoms lying 30–70 m below the seafloor. The magnetic signature of the valleys is due to high magnetic susceptibility volcanic detritus derived from the erosion of the volcanic rocks that overlie most of the salt bodies in this area. This study shows that magnetic data may be a useful tool for mapping surficial sediments. These magnetic data provide the first offshore evidence for the direction of major water discharge. It remains unclear whether the valleys were formed by river incision during one or several regional lowstand(s) or from one or several sub-glacial discharge episode(s) across the Magdalen Plateau. The valley evidence should be incorporated into any future palaeogeographical reconstructions.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.271
Teacher spread0.226 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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