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Record W2624172583 · doi:10.1002/9781119227250.ch18

Best Practices for Reporting Atom Probe Analysis of Geological Materials

2017· other· en· W2624172583 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical monograph · 2017
Typeother
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsGeologyAtom (system on chip)Earth scienceGeochemistryGeophysicsMineralogyEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

The application of atom probe tomography (APT) within the Earth and planetary sciences has produced novel data sets that answer fundamental questions about the near-atomic scale distribution of elements and isotopes within minerals. It involves the incremental evaporation, detection, and subsequent computer reconstruction of charged particles from a needle-shaped specimen. The range of applications is growing such that protocols for reporting are needed for APT data comparison and quality assessment among natural materials. A particular challenge of APT science relates to documenting the instrumental and analyst-dependent conditions that affect the mass spectral and spatial qualities of the data and their interpretation. This contribution outlines recommended data reporting procedures for publication of ATP data in terms of the sample preparation, data collection, and reconstruction phases as well as the characterization and interpretation of the reconstructed volume. Coordinated reporting of this basic information will promote efficient communication of protocols, and aid in the evaluation of published atom probe data as geologists continue to explore atomic compositions and distributions at nanoscale.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.045
GPT teacher head0.328
Teacher spread0.283 · 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