MétaCan
Menu
Back to cohort
Record W2127214139 · doi:10.1017/s0885715615000111

Quantification of stacking disordered Si–Al layer silicates by the Rietveld method: application to exploration for high-sulphidation epithermal gold deposits

2015· article· en· W2127214139 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePowder Diffraction · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
FundersBarrick Gold Corporation
KeywordsPyrophylliteStackingRietveld refinementMineralKaoliniteMaterials scienceMineralogyPhase (matter)Powder diffractionLayer (electronics)GeologyCrystallographyCrystal structureChemistryNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

Hydrothermally altered rocks hosting precious metal deposits frequently contain stacking disordered layer silicates. X-ray diffraction analysis using the Rietveld method can be used to determine mineral abundances in these rocks if suitable disorder models are applied. It is shown here that disorder models of kaolinite and pyrophyllite can be described by a recursive calculation of structure factors. This permits the physically sound refinement of real structure parameters of these disordered minerals and the determination of mineral abundances. Even mixtures containing two disordered Si–Al layer silicates can be quantified reliably. The developed disorder models can now be implemented in routine phase analysis, allowing the quantification of large numbers of samples to identify mineralogical gradients surrounding ore deposits.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.581

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 head0.044
GPT teacher head0.279
Teacher spread0.234 · 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