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Record W3090088696 · doi:10.1190/segam2020-3427249.1

2D forward gravity and magnetic modeling of the Porcupine assemblage, Matheson area

2020· article· en· W3090088696 on OpenAlexaff
Fabiano Della Justina, Richard S. Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPorcupineAssemblage (archaeology)GeologyGeophysicsPaleontology

Abstract

fetched live from OpenAlex

2D forward modeling of gravity and magnetic data has been carried out on the Matheson area, which is located in the southern part of the Abitibi Greenstone Belt, to resolve the subsurface geometry of the Porcupine metasedimentary assemblage. Seismic sections, petrophysical data compilations, and geological maps have been utilized to construct the initial model and provide bounds on the physical properties of the lithologies. Four parallel cross-sections were generated providing a 3D perspective of the Porcupine assemblage geometry at depth and its relation to the surrounding lithology. The modeling provides a check on the validity of the seismic interpretation and can also be used to infer interfaces which are not visible on the seismic sections, due to either subvertical orientation or weak acoustic impedance contrast. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 9:20 AM Presentation Time: 11:00 AM Location: Poster Station 6 Presentation Type: Poster

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 score0.085
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.203
Teacher spread0.173 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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