MétaCan
Menu
Back to cohort
Record W2330842223 · doi:10.5194/acpd-12-30409-2012

Relationship between snow microstructure and physical and chemical processes

2012· article· en· W2330842223 on OpenAlexaff
Thorsten Bartels‐Rausch, Hans‐Werner Jacobi, Tara F. Kahan, Jennie L. Thomas, Erik S. Thomson, Jonathan P. D. Abbatt, Markus Ammann, Jane R. Blackford, Hendrik Bluhm, C. S. Boxe, Florent Dominé, M. M. Frey, Ivan Gladich, Marcelo I. Guzmán, Dominik Heger, Thomas Huthwelker, Petr Klán, W. F. Kuhs, M. H. Kuo, Sönke Maus, Samar G. Moussa, V. Faye McNeill, John T. Newberg, Jan B. C. Pettersson, Martina Roeselová, John R. Sodeau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversité LavalSNC-Lavalin (Canada)Takuvik Joint International LaboratoryUniversity of Toronto
FundersGöteborgs UniversitetCentre National de la Recherche ScientifiqueSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsSnowReactivity (psychology)Environmental scienceScale (ratio)Focus (optics)MeteorologyGeographyPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract. Ice and snow in the environment are important because they not only act as a host to rich chemistry but also provide a matrix for physical exchanges of contaminants within the ecosystem. This review discusses how the structure of snow influences both chemical reactivity and physical processes, which thereby makes snow a unique medium for study. The focus is placed on impacts of the presence of liquid and surface disorder using many experimental studies, simulations, and field observations from the molecular to the micro-scale.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 head0.027
GPT teacher head0.259
Teacher spread0.232 · 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 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

Citations29
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicnanoparticles nucleation surface interactionsFrench-language works237,207