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Record W2018594124 · doi:10.1088/0957-0233/17/6/017

Production and visualization of quaternary combinatorial thin films

2006· article· en· W2018594124 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMeasurement Science and Technology · 2006
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsQuaternarySoftwareComponent (thermodynamics)Combinatorial synthesisMaterials scienceDeposition (geology)Thin filmSputter depositionComputer scienceVisualizationDiffractionComputational scienceNanotechnologySputteringCombinatorial chemistryPhysicsData miningOpticsChemistryGeology

Abstract

fetched live from OpenAlex

A new method for the production of four-component (element or compound) combinatorial thin films is presented. The combinatorial thin film consists of nine continuous four-component regions producing planes in quaternary composition space. The method allows for the coverage of large continuous regions of quaternary or pseudoquaternary systems. The magnetron sputtering deposition method is mechanically simple and uses existing infrastructure. New software for the visualization of quaternary data is also presented. This software allows the straightforward identification of composition-dependent properties such as x-ray diffraction intensities. The new production method and software are demonstrated with a four-element combinatorial thin film containing Si, Sn, Co and C.

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)0.002
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 score0.018
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.235
Teacher spread0.221 · 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