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Record W2080339570 · doi:10.1115/imece2014-38776

Surface Tribology Study Resulting by Diamond-Like Carbon Coating

2014· article· en· W2080339570 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

VenueVolume 2B: Advanced Manufacturing · 2014
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSurface roughnessMaterials scienceCoatingTribologySurface finishDiamond-like carbonComposite materialNanotechnologyThin film

Abstract

fetched live from OpenAlex

Companies that coat their products with DLC often have strict surface roughness and goals. This research investigates the surface roughness properties of uncoated and DLC coated specimens in an effort to know what uncoated surface roughness is needed to obtain a certain DLC coated surface roughness. Therefore, a model describing the relationship between uncoated and DLC coated surface roughness is needed. If this relationship can be estimated, the cost of surface finishing can be minimized by avoiding any unnecessary processes. A total of 7 specimens were tested before and after coating process with a non-contact surface roughness measurement microscope. Mathematical relationships are found between the DLC coated surface roughness and uncoated surface roughness. An experimental methodology was described for applying the findings to other coating methods and materials as the mathematical relationships found in this study are specific to the coating process and materials used.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.007
GPT teacher head0.204
Teacher spread0.196 · 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