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Record W1930607044 · doi:10.1002/grl.50734

Distribution of natural halocarbons in marine boundary air over the Arctic Ocean

2013· article· en· W1930607044 on OpenAlexaff
Yoko Yokouchi, Jun Inoue, D. Toom‐Sauntry

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersJST-Mirai ProgramNational Oceanic and Atmospheric Administration
KeywordsDimethyl sulfideBromoformArcticEnvironmental scienceArctic geoengineeringMethyl iodideOceanographyAtmospheric sciencesDimethylsulfoniopropionateAtmosphere (unit)TroposphereBromideEnvironmental chemistryClimatologyChemistryMeteorologyArctic ice packGeologyGeographyPhytoplankton

Abstract

fetched live from OpenAlex

Ongoing environmental changes in the Arctic will affect the exchange of natural volatile organic compounds between the atmosphere and the Arctic Ocean. Among these compounds, natural halocarbons play an important role in atmospheric ozone chemistry. We measured the distribution of five major natural halocarbons (methyl iodide, bromoform, dibromomethane, methyl chloride, and methyl bromide) together with dimethyl sulfide and tetrachloroethylene in the atmosphere over the Arctic Ocean (from the Bering Strait to 79°N) and along the cruise path to and from Japan. Methyl iodide, bromoform, and dibromomethane were most abundant near perennial sea ice in air masses derived from coastal regions and least abundant in the northernmost Arctic, where the air masses had passed over the ice pack, whereas methyl chloride and methyl bromide showed the opposite distribution pattern. Factors controlling those distributions and future prospects for natural halocarbons in the Arctic are discussed.

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 score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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