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Record W4414224305 · doi:10.5724/gcs.38.107

Shale compositional trends in the Cenomanian–Turonian Cretaceous Western Interior Seaway: facies and sequence stratigraphic models

2025· book-chapter· en· W4414224305 on OpenAlexaffabout
Bruce S. Hart, Michaël Hofmann, Guy Plint, M P B Nicolas

Bibliographic record

VenueGulf Coast Section SEPM eBooks · 2025
Typebook-chapter
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of CanadaWestern University
Fundersnot available
KeywordsSiliciclasticMarine transgressionFaciesDeposition (geology)Sequence (biology)ShoreCretaceousOil shalePelagic sediment

Abstract

fetched live from OpenAlex

ABSTRACT Predictive facies and sequence-stratigraphic models (Hart 2015, 2016) for fine-grained sediment deposition associated with a peak transgression in an epeiric seaway have been tested here using elemental, mineralogical, TOC, and wireline log data. Our samples were collected from close to the Cenomanian–Turonian boundary from five widely dispersed locations (Texas, New Mexico, Colorado, Alberta, Manitoba). We collected data from a range of lithotypes (shales, marlstones, and limestones) from both core and exposures to test the models. Profiles of major elements (Si, Al, Ca, etc.) and TOC show distinctive trends depending on the relative proximity of the location to the shoreline at peak transgression. Siliciclastic minerals (and hence elements such as Si, Al, K) dominate the section in our most proximal location whereas peak transgression was dominated by deposition of pelagic carbonates in distal locations. Organic-carbon content is variably related to sediment composition, again depending on the relative proximity of the location to the shoreline at peak transgression. TOC is highest in marlstones (deposited where pelagic and siliciclastic sediments mixed) and decreases both in basin-center limestones and in proximal, siliciclastic shales.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.238
Teacher spread0.207 · 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.

Study designSimulation or modeling
Domainnot available
GenreOther

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
Published2025
Admission routes2
Has abstractyes

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