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Record W2897808192 · doi:10.1016/j.crte.2018.08.008

Observing the atmospheric evolution of ozone-depleting substances

2018· article· en· W2897808192 on OpenAlexaboutno aff
Stefan Reimann, James W. Elkins, Paul J. Fraser, B. D. Hall, Michael J. Kurylo, Emmanuel Mahieu, S. A. Montzka, Ronald G. Prinn, Matthew Rigby, Peter G. Simmonds, Ray F. Weiss

Bibliographic record

VenueComptes Rendus Géoscience · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UK
KeywordsMontreal ProtocolEnvironmental scienceTroposphereOzoneAtmosphere (unit)MeteorologyAtmospheric chemistryAtmospheric sciencesOzone layerGeographyGeology

Abstract

fetched live from OpenAlex

The atmospheric observations of ozone-depleting substances (ODSs) have been essential for following their atmospheric response to the production and use restrictions imposed by the Montreal Protocol and its Amendments and Adjustments. ODSs have been used since the first half of the 20th century in industrial and domestic applications. However, their atmospheric growth went unnoticed until the early 1970s, when they were discovered using gas chromatograph-electron capture detection (GC-ECD) instruments. Similar instrumentation formed the basis of global flask and in situ measurements commenced by NOAA and ALE/GAGE/AGAGE in the late 1970s. The combination of these networks, supported by a number of other laboratories, has been essential for following the tropospheric trends of ODSs. Additionally, ground-based remote sensing measurements within NDACC and aircraft-based observation programs have been crucial for measuring the evolution of the ODS abundances over the entire atmosphere. Maintaining these networks at least at their current state is vital for ensuring the on-going verification of the success of the Montreal Protocol.

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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.225
Teacher spread0.206 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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