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Record W2275099390 · doi:10.1002/chem.201680162

Frontispiece: Synthesis and Characterisation of Fluorescent Carbon Nanodots Produced in Ionic Liquids by Laser Ablation

2015· paratext· en· W2275099390 on OpenAlexaff
Hemerson P. S. Castro, Virgínia S. Souza, Jackson D. Scholten, Janine H. Dias, Jesum Alves Fernandes, Fabiano Severo Rodembusch, Roberto dos Reis, Jaı̈rton Dupont, Sérgio R. Teixeira, Ricardo R. B. Correia

Bibliographic record

VenueChemistry - A European Journal · 2015
Typeparatext
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFluorescenceNanodotIonic liquidLaser ablationFabricationCarbon fibersMaterials scienceGraphiteLuminescenceLaserAblationIonic bondingNanotechnologyChemical engineeringAnalytical Chemistry (journal)OpticsOptoelectronicsChemistryIonChromatographyOrganic chemistryPhysicsComposite material

Abstract

fetched live from OpenAlex

Carbon Nanodots The fabrication of fluorescent C-dots by laser ablation from graphite powder dispersed in ionic liquids is described. The final luminescent solution illuminated by a violet laser is shown in inset. For more details, see the Full Paper by J. D. Scholten, J. Dupont, R. R. B. Correia et al. on page 138 ff.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.115
Threshold uncertainty score0.385

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1150.097

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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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 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

Citations2
Published2015
Admission routes1
Has abstractyes

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