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Record W4416879667 · doi:10.37665/wexvevh47362

Conformal Coatings - Understanding How Key Properties Translate to Fitness for Use

2025· article· W4416879667 on OpenAlexaboutno aff
Michael A. Strong

Bibliographic record

VenueOn-Demand Webinars · 2025
Typearticle
Language
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsKey (lock)Presentation (obstetrics)Simple (philosophy)Conformal map

Abstract

fetched live from OpenAlex

ABSTRACT Hosted by SMTA Ontario Chapter On their basic premise, conformal coatings are simple materials meant to protect circuit boards against common environmental pollutants and thus extend the lifespan of electronics. With each application, there comes unique requirements that sets a threshold the candidate material must fulfill to have success in the field, but how can we best determine whether the data on paper will correlate? In this webinar, we will explore how to systematically assess a candidate material's suitability for use, comparing common application requirements with standard data along and industry certifications. What's Included: Recorded Presentation (On-Demand)

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)0.001
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 score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.056
GPT teacher head0.254
Teacher spread0.198 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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