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Tropospheric trace gas slant column densities derived from MAX-DOAS measurements on pacific transit cruises of the German research vessel Sonne in 2019

2020· article· en· W3110594638 on OpenAlexaboutno aff
Steffen Dörner, Thomas Ruhtz, Sebastian Donner, Steffen Beirle, Stefan Kinne, Thomas Wagner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsDifferential optical absorption spectroscopyTrace gasOceanographyEnvironmental scienceResearch vesselAtmosphere (unit)TroposphereAtmospheric sciencesGeographyMeteorologyAbsorption (acoustics)GeologyPhysics

Abstract

fetched live from OpenAlex

Between January and July 2019 the German research vessel Sonne was on several cruises in the Pacific, crossing the ocean from Suva, Fiji to Manzanillo, Mexico in February (SO267-2) and from Vancouver, Canada to Singapore in June (SO268-3). A Multi Axis-Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument was in operation outside the national exclusive economic zone (EEZ) regions allowing for profile measurements of trace gases and aerosol on the open seas under background conditions. Both transit cruises cover a wide range of marine biomes and climatic zones affecting the trace gas and particle composition of the atmosphere. Ship measurements of Nitrogen Dioxide (NO2) and Sulphur Dioxide (SO2) are especially important for the validation of satellite measurements as the remote Pacific Ocean is typically used as a reference region. Off the coast of North America an enhanced signal of halogen species, i.e. bromine oxide (BrO) and iodine oxide (IO) was observed. The abundance of formaldehyde (HCHO) and its interrelation with the marine bio-activity could also be observed.

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 score0.127
Threshold uncertainty score0.252

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 head0.071
GPT teacher head0.271
Teacher spread0.199 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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