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Study on the Preparation Method of Fluorescent Rock Thin Sections Made from Non-fluorescent Materials

2023· article· en· W4376608687 on OpenAlexaboutno aff
Guojun Wang

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsFluorescenceGrindingMaterials scienceColloidAbrasiveAdhesiveComposite materialEpoxyMetallurgyChemistryOpticsOrganic chemistryLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Canadian gum, epoxy resin, and emery are used in the production of general rock thin section materials. These materials have fluorescence, so it is not possible to make fluorescent thin sections. The use of other colloids or transparency is not good, because the use of abrasive emery will also lead to the specimen having fluorescence, which cannot be selected. At present, 502 is usually used to make fluorescent sheet adhesive in China, but the colloid is soluble in water and the colloid is not impact-resistant. During sample preparation, it cannot be used for a long time by water grinding, only dry grinding or oil grinding. The samples of water grinding are susceptible to moisture and scratches, so the rock sheet cannot be preserved for a long time, which seriously affects the efficiency and quality of production. In recent years, the team has come up with a variety of materials and methods to make fluorescent rock-thin sections through repeated trials and achieved good results.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.286
Teacher spread0.249 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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