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Record W3047170633 · doi:10.2110/jsr.2020.50

Linking marine core lithofacies and mineral and grain-size compositions on the Baffin Island margin: changes in provenance and transport

2020· article· en· W3047170633 on OpenAlexaff
John T. Andrews, Kimberley A. Jenner, D C Campbell

Bibliographic record

VenueJournal of Sedimentary Research · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsGeologyProvenancePlagioclaseCarbonateFaciesMineralogyGeochemistryClay mineralsKaoliniteQuartzCarbonate mineralsCalciteQuaternaryGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

ABSTRACT We evaluate the linkages between lithofacies and mineral composition of late Quaternary sediments along the Baffin Slope for cores 2013029 64, 74, and 77. Four major lithofacies were identified: diamicton (L1), laminated red-brown mud (L2), tan carbonate mud (L3), and brown bioturbated mud (L4). In addition, gold-brown mud (L2a) beds were identified within red-brown mud throughout the Baffin margin and a thin, locally distributed light gray mud (L2b), also identified within red-brown mud, was localized to the Home Bay region. A classification decision tree (CDT) correctly predicted ∼ 87% of the lithofacies based on five binary choices based on the estimated weight %s of (in order): quartz, kaolinite, plagioclase, iron oxides, and smectites. The detrital tan carbonate (DC) minerals, calcite and dolomite, did not appear in the chosen CDT solution although this lithofacies is easily recognized in cores because of its tan color and the facies is well predicted in the CDT. The addition of grain size did not substantially improve the prediction of the lithofacies although it did change the % importance of the minerals in the CDT.

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 score0.057
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.293
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 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

Citations10
Published2020
Admission routes1
Has abstractyes

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