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Record W4200092745 · doi:10.1038/s42004-021-00616-w

Open questions on transition metals driving secondary thermal processes in atmospheric aerosols

2021· article· en· W4200092745 on OpenAlexafffund
Hind A. Al‐Abadleh, Sergey A. Nizkorodov

Bibliographic record

VenueCommunications Chemistry · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaWilfrid Laurier University
KeywordsAerosolEnvironmental scienceAtmospheric sciencesAtmosphere (unit)Climate changeThermalEarth scienceChemical physicsEnvironmental chemistryAstrobiologyMeteorologyChemistryPhysicsGeologyOceanography

Abstract

fetched live from OpenAlex

Transition metals are increasingly recognized as key drivers in the formation and aging of light-absorbing organic aerosols, known as brown carbon, which impact the energy flux in the atmosphere. Here the authors discuss somewhat overlooked condensed phase chemical processes and identify research needs to improve our fundamental understanding of atmospheric aerosols and ultimately reduce modelling uncertainties of the direct and indirect effects of aerosol particles on the climate.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.010
Open science0.0030.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.024
GPT teacher head0.260
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 designTheoretical or conceptual
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

Citations13
Published2021
Admission routes2
Has abstractyes

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