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Record W2900258110 · doi:10.1029/2018gl079784

Phosgene in the Upper Troposphere and Lower Stratosphere: A Marker for Product Gas Injection Due to Chlorine‐Containing Very Short Lived Substances

2018· article· en· W2900258110 on OpenAlexaffabout
Jeremy J. Harrison, Martyn P. Chipperfield, Ryan Hossaini, C. D. Boone, Sandip Dhomse, Wuhu Feng, P. F. Bernath

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Waterloo
FundersNatural Environment Research CouncilSight Research UKUK Research and InnovationNational Aeronautics and Space Administration
KeywordsPhosgeneStratosphereTroposphereOzoneChlorineAtmospheric sciencesAtmospheric chemistryOzone depletionOzone layerChemistryCarbon tetrachlorideEnvironmental scienceMeteorologyPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Phosgene in the atmosphere is produced via the degradation of carbon tetrachloride, methyl chloroform, and a number of chlorine‐containing very short lived substances (VSLS). These VSLS are not regulated by the Montreal Protocol even though they contribute to stratospheric ozone depletion. While observations of VSLS can quantify direct stratospheric source gas injection, observations of phosgene in the upper troposphere/lower stratosphere can be used as a marker of product gas injection of chlorine‐containing VSLS. In this work we report upper troposphere/lower stratosphere measurements of phosgene made by the ACE‐FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) instrument and compare with results from the TOMCAT/SLIMCAT three‐dimensional chemical transport model to constrain phosgene trends over the 2004–2016 period. The 13‐year ACE‐FTS time series provides the first observational evidence for an increase in chlorine product gas injection. In 2016, VSLS accounted for 27% of modeled stratospheric phosgene, up from 20% in the mid‐2000s.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.444
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.290
Teacher spread0.258 · 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 teacher head, 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

Citations27
Published2018
Admission routes2
Has abstractyes

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