MétaCan
Menu
Back to cohort
Record W265012073 · doi:10.5006/c2008-08175

Pharmaceutical Surface Defects: Correlation between Microstructural Artifacts and Electropolishing Results

2008· article· en· W265012073 on OpenAlex
Nate C. Eisinger, R. K. Raney, Richard E. Avery

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsNickel Institute
Fundersnot available
KeywordsElectropolishingMaterials scienceSurface (topology)MetallurgyCorrosionChemistryElectrodeGeometry

Abstract

fetched live from OpenAlex

Abstract With the recent interest in superaustenitic stainless steels and nickel-base alloys for materials of construction in pharmaceutical applications, specifications have been created to assure the best possible finish after electropolishing. These specifications require extensive microstructural examinations for the presence of inclusions and secondary phases. By excluding these phases, electropolishing defects will be minimized. It has been shown that banding of secondary phase will cause significant indications during electropolishing, but there has been minimal evidence to determine the effect of other inclusions. This paper will discuss some of the microstructural defects and their correlation to surface defects. Furthermore, some of the causes for these microstructural defects will be discussed.

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 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 score0.123
Threshold uncertainty score0.531

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)0.0000.000

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 head0.020
GPT teacher head0.236
Teacher spread0.215 · 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