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GGR Biennial Critical Review: Analytical Developments Since 2012

2014· article· en· W2915654371 on OpenAlexaff
Michael Wiedenbeck, L. Paul Bédard, Roxana Bugoi, M. F. Horan, Kathryn L. Linge, Silke Merchel, Luiz F. G. Morales, Dany Savard, A. Kate Souders, Paul Sylvester

Bibliographic record

VenueGeostandards and Geoanalytical Research · 2014
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversité du Québec à Chicoutimi
FundersAstronomical Society of Australia
KeywordsCompendiumData scienceInterpretation (philosophy)NanotechnologyIsotope geochemistryComputer scienceEarth scienceEngineering physicsAnalytical Chemistry (journal)ChemistryMaterials sciencePhysicsGeologyEnvironmental chemistryIsotopeArchaeologyGeographyNuclear physics

Abstract

fetched live from OpenAlex

Advances in the chemical, crystallographic and isotopic characterisation of geological and environmental materials can often be ascribed to technological improvements in analytical hardware or to innovative approaches to data acquisition and/or its interpretation. This biennial review addresses key laboratory methods that form much of the foundation for analytical geochemistry; again, this contribution is presented as a compendium of laboratory techniques. We highlight advances that have appeared since January 2012 and that are of particular significance for the chemical and isotopic characterisation of geomaterials. Prominent scientists from the selected analytical fields present publications they judge to be particular noteworthy, providing background information about the method and assessing where further opportunities might be anticipated. In addition to the well‐established technologies such as thermal ionisation mass spectrometry and plasma emission spectroscopy, this publication also presents new or rapidly growing methods such as electron backscattered diffraction analysis and atom probe tomography – a very sensitive method providing atomic scale information.

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)0.007
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.066
GPT teacher head0.386
Teacher spread0.321 · 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 designNot applicable
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

Citations14
Published2014
Admission routes1
Has abstractyes

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