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Record W6966778345 · doi:10.48380/dggv-txjf-pf94

The spectroscopy of luminescent geological materials

2021· article· en· W6966778345 on OpenAlexaff

Bibliographic record

Venuedggv-e-publications · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMineralogy and Gemology Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsLuminescenceSpectroscopyExcited statePhotoluminescenceEmission spectrumActivator (genetics)ExcitationElectron spectroscopyPersistent luminescence

Abstract

fetched live from OpenAlex

Luminescence is the non-incandescent emission of light from materials excited by an electron beam. Electron irradiation raises sample electrons to an excited state, which then emit a photon as they return to a lower energy. Luminescence phenemona may be studied in several ways, including spectral and spatial methods. Whilst cathodolominescence (CL) has become an established method of analysis for Earth materials, other forms of luminescence in minerals should not be overlooked. Photoluminescence (PL) studies, for example, allow for emission and excitation spectroscopy to be examined in weak- to strongly luminescent minerals, such as wilmenite. In the case of quartz PL, emission spectroscopy investigations have shown that excitation at different wavelengths produces highly variable emission spectra that relate to one or more transitions for excitation. As the building blocks to rocks, minerals and the atoms or ions within preserve critical information concerning the conditions attending growth or subsequent evolution - thus, investigation of these can inform on the origin or surface/near surface interactions relating to environmental change. As a non-destructive technique used in the study of rare materials (e.g., Lunar meteorites) luminescence imaging and spectroscopy have the potential to help characterise, as well as, elucidate domains or reveal fine-scale features not resolvable by optical methods. Advances in instrumentation now permit the collection of multi-dimensional data sets (e.g., hyperspectral) that can be interrogated off-line. The simultaneous capture and interpretation of compositional and luminescence signals has the potential to greatly improve our understanding of causes of luminescence, be these trace activator or defect.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.996

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)0.0040.000

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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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 designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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