MétaCan
Menu
Back to cohort
Record W2769881579 · doi:10.1088/1748-9326/aa9c87

Current sources of carbon tetrachloride (CCl <sub>4</sub> ) in our atmosphere

2017· article· en· W2769881579 on OpenAlexaboutno aff
David F. Sherry, Archie McCulloch, Qing Liang, Stefan Reimann, Paul A. Newman

Bibliographic record

VenueEnvironmental Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersGoddard Space Flight CenterNational Aeronautics and Space Administration
KeywordsCarbon tetrachlorideEnvironmental scienceAtmosphere (unit)Raw materialMontreal ProtocolEnvironmental chemistryChloromethaneGreenhouse gasCarbon fibersOzoneOzone layerChemistryMeteorologyMaterials scienceGeographyCatalysis

Abstract

fetched live from OpenAlex

Carbon tetrachloride (CCl 4 or CTC) is an ozone-depleting substance whose emissive uses are controlled and practically banned by the Montreal Protocol (MP). Nevertheless, previous work estimated ongoing emissions of 35 Gg year −1 of CCl 4 into the atmosphere from observation-based methods, in stark contrast to emissions estimates of 3 (0–8) Gg year −1 from reported numbers to UNEP under the MP. Here we combine information on sources from industrial production processes and legacy emissions from contaminated sites to provide an updated bottom-up estimate on current CTC global emissions of 15–25 Gg year −1 . We now propose 13 Gg year −1 of global emissions from unreported non-feedstock emissions from chloromethane and perchloroethylene plants as the most significant CCl 4 source. Additionally, 2 Gg year −1 are estimated as fugitive emissions from the usage of CTC as feedstock and possibly up to 10 Gg year −1 from legacy emissions and chlor-alkali plants.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.266
Teacher spread0.242 · 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

Citations77
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Research LettersSame topicAtmospheric chemistry and aerosolsFrench-language works237,207