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Record W2989633833 · doi:10.1029/2019gc008818

The Henkel Petrophysical Plot: Mineralogy and Lithology From Physical Properties

2020· article· en· W2989633833 on OpenAlexaff
R J Enkin, T S Hamilton, William A. Morris

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsMcMaster UniversityGeological Survey of Canada
Fundersnot available
KeywordsGeologyMagnetiteIgneous rockMineralogyPetrophysicsFeldsparLithologyGeochemistryMineralParamagnetismQuartzMagnetic susceptibilityRock magnetismGeophysicsMagnetizationRemanencePorosityMaterials scienceChemistryMagnetic field

Abstract

fetched live from OpenAlex

Abstract The Henkel plot (logarithm of magnetic susceptibility versus density of rock samples) reveals that most rocks fall on either a “magnetite trend” or a “paramagnetic trend.” Interpretation of gravity and magnetic surveys is improved when the mineralogical and lithological basis of these trends is understood. We present a quantitative mineralogical mixing model, involving the components QFC (quartz‐feldspar‐calcite), FM (ferromagnesian silicates), and M (magnetite) and discuss the geological processes which produce or modify these mixtures. Igneous rocks mostly plot on the magnetite trend, where the FM/M ratio is about 10. The density‐susceptibility mineralogical mixing model is compatible with the CIPW mineral calculation for igneous classification from chemical analyses. Sedimentary and metamorphic processes usually involve oxidation, reduction, and/or iron loss, all which are magnetite‐destructive and lead to petrophysical measurements along the paramagnetic trend where FM/M > 1,000. Mineralization, with the introduction of sulfides and oxides, leads to dense rocks which do not plot along the magnetite nor paramagnetic trends. This quantitative analysis provides a method to integrate geological processes in the interpretation of geophysical surveys.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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 designBench or experimental
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

Citations33
Published2020
Admission routes1
Has abstractyes

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