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Record W7009109668

3D structural geological model of the Mallik Anticline, Canadian Arctic

2023· report· en· W7009109668 on OpenAlexaboutno aff

Bibliographic record

VenuePublication Database GFZ (GFZ German Research Centre for Geosciences) · 2023
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsAnticlinePermafrostArcticSedimentary rockTectonicsDeltaGeologic mapBaySequence (biology)Clastic rock
DOInot available

Abstract

fetched live from OpenAlex

The Mallik Anticline is a geologic structure in the Mackenzie Delta in the Canadian Arctic. Tectonics throughout the Cenozoic, with compressional phases in the early Eocene to the late Miocene, formed this large, domed structure that is today an important source of hydrocarbons. Gas hydrates occur in the clastic sedimentary rocks of the Oligocene to Pleistocene Kugmallite, Mackenzie Bay, and Iperk sequences, which were essentially formed by deltaic processes. The presence of hydrocarbon gases within the permafrost zone in the Canadian Arctic has led to extensive exploration and production activities in the region since the mid-1960s, and the investigations by geologists and geophysicists have already been published in numerous scientific articles to date. The associated report (Chabab and Kempka, 2023) describes the implementation of the first field-scale 3D static geologic model of the Mallik site, which was created using data from well logs and 2D seismic reflection profiles. The dataset presented here provides elevation depths and thickness data of the three distinct sequence boundaries Kugmallit-Richards, Mackenzie Bay-Kugmallit and Iperk-Mackenzie Bay as well as fault data from the Mallik site.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.170
GPT teacher head0.359
Teacher spread0.189 · 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 designSimulation or modeling
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 routes1
Has abstractyes

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