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Record W2009544213 · doi:10.1002/cvde.200304153

Growth of IrO<sub>2</sub> Films and Nanorods by Means of CVD: An Example of Compositional and Morphological Control of Nanostructures

2003· article· en· W2009544213 on OpenAlex
R.S. Chen, Yen‐Jung Chen, Yao‐Zeng Huang, Yen‐An Chen, Yün Chi, C.‐S. Liu, K. K. Tiong, Arthur J. Carty

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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

VenueChemical Vapor Deposition · 2003
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsNanorodNanostructureIridiumTorrMaterials scienceMorphology (biology)NanotechnologyDeposition (geology)Oxygen pressureOxygenThin filmChemical engineeringChemical vapor depositionMineralogyChemistryCatalysisGeologyPhysics

Abstract

fetched live from OpenAlex

Conductive iridium oxide films or one‐dimensional nanorods are deposited using (MeCp)Ir(COD). A systematic investigation is carried out, showing a close correlation of morphology with deposition pressure and oxygen partial pressure. Of particular interest, are the vertically aligned IrO 2 nanorods (see Figure) deposited at 350 °C in 30 torr of oxygen. The surface morphology and structural composition of the materials are confirmed.

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)0.000
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 score0.003
Threshold uncertainty score0.438

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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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