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Record W2012399383 · doi:10.1039/b516323d

Polycyclic aromatic hydrocarbons, carbon nanoparticles and the diffuse interstellar bands

2006· article· en· W2012399383 on OpenAlexafffund
W. W. Duley

Bibliographic record

VenueFaraday Discussions · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbon fibersContext (archaeology)Carbon NanoparticlesAstrochemistryExtinction (optical mineralogy)Abundance (ecology)MoleculeInterstellar mediumChemical physicsNanoparticlePhysicsChemistryMaterials scienceAstrophysicsNanotechnologyMineralogyOrganic chemistryPaleontologyGeologyGalaxy

Abstract

fetched live from OpenAlex

Observational data on the appearance and properties of the diffuse interstellar bands (DIBs) are reviewed in the context of a model in which the proposed carriers of these bands are large carbon molecules and carbon nanoparticles containing between 30 and several hundred carbon atoms. The abundance of these carriers, as estimated from the observed strengths of the DIBs, place strong constraints on their rates of formation and destruction, and suggest that the strongest bands, including that at 4428 A, could be produced via the decomposition of larger carbon particles, possibly those particles that have been postulated to be the source of the 2175 A extinction feature. Such particles are of mixed sp2 and sp3 carbon composition, with sizes between that of large molecules and small macroscopic solids. Any description of their characteristics must combine aspects of molecular and condensed matter physics, and this is incorporated in the present discussion. I discuss recent experimental and theoretical data related to these matters.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.202
Teacher spread0.198 · 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

Citations88
Published2006
Admission routes2
Has abstractyes

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