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Record W2556435845 · doi:10.1002/smll.201670232

Liquid Materials: 3D Printing of Highly Conductive Nanocomposites for the Functional Optimization of Liquid Sensors (Small 44/2016)

2016· article· en· W2556435845 on OpenAlexaff
Kambiz Chizari, Mohamed Amine Daoud, Anil R. Ravindran, Daniel Therriault

Bibliographic record

VenueSmall · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
Keywords3D printingMaterials sciencePipeline (software)Scaffold3d printedNanotechnologyElectrical conductorJoint (building)LeakNanocompositeOptoelectronicsComposite materialMechanical engineeringBiomedical engineeringEngineering

Abstract

fetched live from OpenAlex

On page 6076, D. Therriault and co-workers demonstrate a 3D printing method used to make a liquid sensor with a potential application in oil industry. The cover image shows a 3D printing machine printing a scaffold structure around a pipeline joint with an electronic device as a leak detector on top of the joint. The background is a SEM image of a 3D printed scaffold, used as a liquid sensor.

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 score0.033
Threshold uncertainty score0.293

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 head0.044
GPT teacher head0.241
Teacher spread0.197 · 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.

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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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