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Record W4362608779 · doi:10.5194/acp-2023-53-rc2

Comment on acp-2023-53

2023· peer-review· en· W4362608779 on OpenAlexaboutno aff
G. Dreyfus, S. A. Montzka, Stephen O. Andersen, Richard Ferris

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
FundersChildren's Investment Fund Foundation
KeywordsMontreal ProtocolOzone layerEnvironmental scienceGreenhouse gasOzoneOzone depletionEnvironmental protectionMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract. By phasing out production and consumption of most ozone depleting substances (ODSs), the Montreal Protocol on Substances that Deplete the Ozone Layer (Montreal Protocol) has avoided consequences of increased ultraviolet (UV) radiation, and it will restore stratospheric ozone to pre-1980 conditions by mid-century, assuming compliance with the phaseout. However, several studies have documented an unexpected increase in emissions and unreported production of trichlorofluoromethane (CFC-11) and other ODSs that occurred after 2012 despite production phaseouts under the Montreal Protocol. Furthermore, because most ODSs are powerful greenhouse gases there are significant climate protection benefits in collecting and destroying the substantial quantities of historically allowed products under the Montreal Protocol that are contained in existing equipment and products and referred to as ODS “banks”. Here we present a framework for considering offsets to ozone depletion, climate forcing, and other environmental impacts arising from this or other occurrences of unexpected emissions and unreported production of Montreal Protocol controlled substances. We also show how this methodology could be applied to the destruction of banks of controlled ODSs and GHGs, or to halon or other production allowed under a Montreal Protocol Essential Use Exemption or emergency exemption. Further, we explore a range of potential actions that could offset the ozone depletion, climate, and other environmental impacts arising from instances of unexpected emissions or unreported production should Montreal Protocol Parties agree require remedial action.

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)0.002
metaresearch head score (Gemma)0.013
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.132
Threshold uncertainty score0.443

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0390.019
Insufficient payload (model declined to judge)0.1320.120

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 head0.287
GPT teacher head0.333
Teacher spread0.045 · 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
GenreCommentary

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

Citations0
Published2023
Admission routes1
Has abstractyes

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