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Record W2626507900 · doi:10.1016/s2542-5196(17)30040-2

Anaesthetic gases, climate change, and sustainable practice

2017· article· en· W2626507900 on OpenAlexaboutno aff
M. Charlesworth, Frank Swinton

Bibliographic record

VenueThe Lancet Planetary Health · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal ProtocolScopusChlorofluorocarbonIsofluraneDesfluraneNitrous oxideGreenhouse gasGlobal-warming potentialOzone layerEnvironmental scienceMedicineAnesthesiaPolitical scienceOzoneMeteorologyGeographyMEDLINELaw

Abstract

fetched live from OpenAlex

Modern anaesthetic gases include the hydrofluorocarbons sevoflurane and desflurane, the chlorofluorocarbon isoflurane, and nitrous oxide. Following use, anaesthetic gases are expelled into the atmosphere, where they contribute to anthropogenic climate change.1–5 Recently, the atmospheric concentrations of anaesthetic gases have been determined, and the most damaging agent, desflurane, is rapidly increasing.1 The Montreal protocol aims to phase out global chlorofluorocarbon use, with hydrofluorocarbons subsequently targeted through the 2016 Kigali amendment;6 however, anaesthetic gases are often excluded from such discussions because of their medical necessity.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.006
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0240.005

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 head0.105
GPT teacher head0.351
Teacher spread0.246 · 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 designNot applicable
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

Citations130
Published2017
Admission routes1
Has abstractyes

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