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Record W2329430539 · doi:10.1149/04901.0289ecst

Piecewise-Linear Algorithm Applied to R<sup>2</sup> Experimental Plasma Etching Process

2012· article· en· W2329430539 on OpenAlexaff
V. M. Jiménez-Fernández, C. Reyes‐Betanzo, Zulma Janet Hernández Paxtián

Bibliographic record

VenueECS Transactions · 2012
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsPiecewisePiecewise linear functionComputationInterpolation (computer graphics)Geodetic datumAlgorithmProcess (computing)MathematicsPower (physics)Data pointCurve fittingComputer scienceMathematical analysisPhysicsStatistics

Abstract

fetched live from OpenAlex

Sometimes the only information that is available to characterize an experimental process is a collection of input-output data. In those cases, piecewise linear (PWL) interpolation is an optimal strategy to fit the data into a curve. This paper computing plasma parameters of pressure and RF power as input data that corresponds with a specific silicon etch rate as the output datum in a two dimensional experiment whose behavior is described by a R 2 PWL curve. The computation is approached by a numerical algorithm which scans all the discrete input data distributed in a simplicial form.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2012
Admission routes1
Has abstractyes

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