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Record W6982098538

Gravity data acquisition and potential-field data modelling along Metal Earth's Chibougamau transect using geophysical and geological constraints

2019· dissertation· en· W6982098538 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2019
Typedissertation
Languageen
FieldMathematics
TopicStatistical Methods and Bayesian Inference
Canadian institutionsnot available
Fundersnot available
KeywordsTransectPetrophysicsPrecambrianData acquisitionMineral explorationMineral resource classification
DOInot available

Abstract

fetched live from OpenAlex

The Metal Earth (ME) project aims to understand the underlying geological mechanisms that
\ndifferentiate mineral endowments in Precambrian greenstone belts of the Canadian Shield. The
\nME project acquires and collates various geological and geophysical data along 13 transects to
\ncreate valid models of subsurface features in order to identify components that contribute to the
\nmineralization processes that result in mineral endowment.
\nIn this thesis, gravity observation along ~128 line kilometers in the Chibougamau transect is
\nconsidered. The acquired data were checked for quality, processed to calculate the complete
\nBouguer anomaly and combined with existing gravity data provided by the Geological Survey of
\nCanada.
\nGravity and compiled magnetic data were forward modelled along four sections and constrained
\nby surficial geological observations, seismic sections, and petrophysical properties to estimate
\nand improve the geometry and depth of plutonic bodies, and identifying the subsurface features
\nsuch as dykes and faults. These improvements will help others to identify components that
\ncontribute to mineralising processes.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.062
GPT teacher head0.303
Teacher spread0.241 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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