MétaCan
Menu
Back to cohort
Record W1981445057 · doi:10.1021/cr500648z

Heterogeneous Photochemistry in the Atmosphere

2015· review· en· W1981445057 on OpenAlexafffund
C. George, Markus Ammann, Barbara D’Anna, D. J. Donaldson, Sergey A. Nizkorodov

Bibliographic record

VenueChemical Reviews · 2015
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersDivision of Atmospheric and Geospace SciencesNational Science FoundationEuropean Research CouncilNatural Sciences and Engineering Research Council of CanadaEuropean CommissionSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsPhoneLibrary scienceCitationChemistryArt historyArtComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Atmospheric aerosols can be categorized into primary particles, which are directly emitted by their sources, and secondary particles, generated in the atmosphere from gaseous inorganic and organic precursors. For example, atmospheric oxidation of sulfur containing compounds leads to sulfuric acid and its salts, which represent a major secondary inorganic component of atmospheric aerosols. Likewise, oxidation of nitrogen oxides leads to nitric acid or its salts, which are also abundant in aerosols. The ocean surface, which covers three-quarters of the planet, offers a remarkably dynamic and chemically complex surface for interfacial reactions in the marine boundary layer. The porous nature of permanent or perennial snowpacks adds a tremendous amount of surface area, with which the atmosphere interacts. In short, solar radiation can provide the energy to initiate reactions while atmospherically available surfaces or condensed phases may act to reduce the required energy for a given photochemical pathway, for instance, by allowing a longer wavelength for reaction of species associated with a surface or bulk phase environment.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.078
GPT teacher head0.315
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations733
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueChemical ReviewsSame topicAtmospheric chemistry and aerosolsFrench-language works237,207