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Record W2169970383 · doi:10.5194/acp-12-6237-2012

Halogen activation via interactions with environmental ice and snow in the polar lower troposphere and other regions

2012· article· en· W2169970383 on OpenAlexaff
Jonathan P. D. Abbatt, J. L. Thomas, Katarina Abrahamsson, C. S. Boxe, Anna Granfors, A. E. Jones, Martin D. King, Alfonso Saiz‐Lopez, P. B. Shepson, John R. Sodeau, D. W. Toohey, Céline Toubin, R. von Glasow, Sumi N. Wren, Xin Yang

Bibliographic record

VenueAtmospheric chemistry and physics · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersAgence Nationale de la RechercheNatural Environment Research CouncilSight Research UK
KeywordsSnowTropospherePolarChemistrySea iceScale (ratio)Environmental scienceClimatologyMeteorologyGeologyGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract. The role of ice in the formation of chemically active halogens in the environment requires a full understanding because of its role in atmospheric chemistry, including controlling the regional atmospheric oxidizing capacity in specific situations. In particular, ice and snow are important for facilitating multiphase oxidative chemistry and as media upon which marine algae live. This paper reviews the nature of environmental ice substrates that participate in halogen chemistry, describes the reactions that occur on such substrates, presents the field evidence for ice-mediated halogen activation, summarizes our best understanding of ice-halogen activation mechanisms, and describes the current state of modeling these processes at different scales. Given the rapid pace of developments in the field, this paper largely addresses advances made in the past five years, with emphasis given to the polar boundary layer. The integrative nature of this field is highlighted in the presentation of work from the molecular to the regional scale, with a focus on understanding fundamental processes. This is essential for developing realistic parameterizations and descriptions of these processes for inclusion in larger scale models that are used to determine their regional and global impacts.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.896

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)0.0010.000

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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 designObservational
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

Citations286
Published2012
Admission routes1
Has abstractyes

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