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Record W2016973229 · doi:10.1002/asl.289

Atmospheric composition of West Africa: highlights from the AMMA international program

2010· article· en· W2016973229 on OpenAlexaff
C. Mari, Claire E. Reeves, G. Ancellet, Maria Dolores Andrés‐Hernández, Brice Barret, J. Bechara, Agnès Borbon, Idir Bouarar, Francesco Cairo, R. Commane, Claire Delon, M. J. Evans, F. Fierli, C. F. A. Floquet, Corinne Galy‐Lacaux, Dwayne E. Heard, C. D. Homan, Trevor Ingham, N. Larsen, Alastair C. Lewis, C. Liousse, J. G. Murphy, E. Orlandi, D. E. Oram, Marielle Saunois, D. Serça, D. J. Stewart, Daniel Stone, V. Thouret, P. van Velthoven, J. E. Williams

Bibliographic record

VenueAtmospheric Science Letters · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersNatural Environment Research CouncilSight Research UK
KeywordsClimatologyEnvironmental scienceMonsoonAtmospheric sciencesOzoneAtmospheric compositionMultidisciplinary approachMeteorologyGeographyAtmosphere (unit)Geology

Abstract

fetched live from OpenAlex

Abstract The atmospheric composition of West Africa reflects the interaction of various dynamical and chemical systems (i.e. biogenic, urban, convective and long‐range transport) with signatures from local to continental scales. Recent measurements performed during the African Monsoon Multidisciplinary Analyses (AMMA) observational periods in 2005 and 2006 provide new data which has allowed new insight into the processes within these systems that control the distribution of ozone and its precursors. Using these new data and recently published results, we provide an overview of these systems with a particular emphasis on ozone distributions over West Africa during the wet season. Copyright © 2010 Royal Meteorological Society

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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

Citations31
Published2010
Admission routes1
Has abstractyes

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