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Record W2557549415 · doi:10.1144/m46.93

Mid-latitude complex trough-mouth fans, Laurentian and Northeast fans, eastern Canada

2016· article· en· W2557549415 on OpenAlexaffabout
David J. W. Piper, D C Campbell, D. Mosher

Bibliographic record

VenueGeological Society London Memoirs · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsTrough (economics)GeologyGeography

Abstract

fetched live from OpenAlex

Submarine fans seaward of mid-latitude glacial ice streams in cross-shelf troughs show a complex evolution. Unlike high-latitude fans where glacigenic debris-flows predominate, meltwater discharge appears to play a larger role in fan evolution, reflected in the growth of high channel-levees and the development of broad, flat-floored submarine valleys. Where canyons are deeply incised, large mass-transport events may be triggered leading to much of the mid-fan area being constructed of mass-transport deposits (MTDs). The Laurentian and Northeast fans are two large submarine fans that lie seawards of major glacial cross-shelf troughs on the continental margin south of Nova Scotia, Canada (Fig. 1a). Both fans span more than 300 km from the shelf break to the Sohm Abyssal Plain. The fans have well-developed channel-levee systems with the western levee of the Laurentian Fan forming a major constructional feature on the margin (Fig. 1c, e). Multibeam and side-scan data coverage of the fans is more complete for the lower fan of Northeast Fan and for the middle and upper fan of Laurentian Fan. Fig. 1. Multibeam bathymetry, backscatter, side-scan and seismic-reflection data over large mid-latitude trough-mouth fans south of …

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)0.000
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 score0.016
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.201
Teacher spread0.184 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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