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Record W3038462304 · doi:10.1017/s143192760003511x

FIB Techniques for Analysis of Metallurgical Specimens

2000· article· en· W3038462304 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

VenueMicroscopy and Microanalysis · 2000
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsFibics (Canada)
Fundersnot available
KeywordsFocused ion beamMaterials scienceIonChemistry

Abstract

fetched live from OpenAlex

Abstract Focused ion beam (FIB) microscopes, the use of which is well established in the semiconductor industry, are rapidly gaining attention in the field of materials science, both as a tool for producing site specific, parallel sided TEM specimens and as a stand alone specimen preparation and imaging tool. Both FIB secondary ion images (FIB SII) and FIB secondary electron images (FIB SEI) contain novel crystallographic and chemical information. The ability to see “orientation contrast” in FIB SEI and to a lesser extent SII is well known for cubic materials and more recently stress-free FIB sectioning combined with FIB imaging have been shown to reveal evidence of plastic deformation in metallic specimens. Particularly in hexagonal metals, FIB orientation contrast is sometimes reduced or eliminated by the FIB sectioning process. We have successfully employed FIB gas assisted etching during FIB sectioning using XeF2 for zirconium alloys and Cl2 for zinc coatings on steels to retain orientation contrast during subsequent imaging.

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 categoriesInsufficient payload (model declined to judge)
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 score0.033
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.279
Teacher spread0.263 · 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