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Record W4385445930 · doi:10.5194/egusphere-2023-1362

Measurement Report: Bio-physicochemistry of tropical clouds at Maïdo (Réunion Island, Indian Ocean): overview of results from the BIO-MAÏDO campaign

2023· preprint· en· W4385445930 on OpenAlexaff
Maud Leriche, Pierre Tulet, Laurent Deguillaume, Frédéric Burnet, Aurélie Colomb, Agnès Borbon, Corinne Jambert, Valentin Duflot, Stéphan Houdier, Jean‐Luc Jaffrezo, Mickaël Vaïtilingom, Pamela Dominutti, Manon Rocco, Camille Mouchel‐Vallon, Samira El Gdachi, Maxence Brissy, Maroua Fathalli, Nicolas Maury, Bert Verreyken, Crist Amelynck, Niels Schoon, Valérie Gros, Jean‐Marc Pichon, Mickaël Ribeiro, Éric Pique, Emmanuel Leclerc, Thierry Bourrianne, Axel Roy, Éric Moulin, Joël Barrié, Jean‐Marc Metzger, Guillaume Péris, Christian Guadagno, Chatrapatty Bhugwant, Jean-Mathieu Tibere, Arnaud Tournigand, Evelyn Freney, Karine Sellegri, Anne‐Marie Delort, Pierre Amato, Muriel Joly, Jean‐Luc Baray, Pascal Renard, Angelica Bianco, Anne Réchou, Guillaume Payen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité du Québec à Montréal
FundersHorizon 2020Belgian Federal Science Policy OfficeAgence Nationale de la RechercheMet Office
KeywordsEnvironmental scienceAtmospheric sciencesRadiative transferChemical transport modelObservatoryTroposphereGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract. The BIO-MAÏDO (Bio-physicochemistry of tropical clouds at Maïdo (Réunion Island): processes and impacts on secondary organic aerosols formation) campaign was conducted from the 13th of March to the 4th of April 2019 on the tropical Réunion Island and implied several scientific teams and state-of-the-art instrumentation. The campaign was part of the BIO-MAÏDO project with the main objective is to improve our understanding of cloud impacts on the formation of secondary organic aerosols (SOA) from biogenic volatile organic compounds (BVOC) precursors in a tropical environment. Instruments were deployed at five sites: a receptor site, the Maïdo observatory (MO) at 2165 m asl, and four sites along the slope of the Maïdo mountain. The obtained dataset includes measurements of the gas-phase mixing ratio of volatile organic compounds (VOC), the characterization of the physical, chemical, and biological (bacterial diversity) properties of aerosols and the characterization of the physical, chemical and biological (identification of viable bacteria through culture-based approaches) properties of the cloud water. In addition, the turbulent parameters of the boundary layer, radiative fluxes, and emissions fluxes of BVOC from the surrounding vegetation were measured to help with the interpretation of the observed chemical concentrations in the different phases. Dynamical analyses show two preferred trajectories routes for air masses arriving at MO during the daytime both corresponding to the return branches of the trade winds associated with the up-slopes thermal breezes. These air masses likely encountered cloud processing during transport along the slope. The highest mixing ratio of oxygenated VOC (OVOC) were measured above the site located in the endemic forest and the highest contribution of OVOC to total VOC at MO. Chemical composition of particles during the daytime shows a higher concentration of oxalic acid and a more oxidized organic aerosol at MO than at other sites along the slope. This is a signature of photochemical aerosols aging along the slope potentially influenced by cloud processing. Despite an in-depth analysis of organic compounds in cloud water, around 80 % on average of dissolved organic compounds is undefined highlighting the complexity of the cloud organic matter.

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)0.000
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 score0.086
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.053
GPT teacher head0.249
Teacher spread0.196 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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