MétaCan
Menu
Back to cohort

Still No Time for Complacency: Evaluating the Ongoing Success and Continued Challenge of Global Ozone Policy

2015· article· W7134234292 on OpenAlexaboutno aff
David Leonard Downie

Bibliographic record

VenueDigitalCommons - Fairfield (Fairfield University) · 2015
Typearticle
Language
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsOzone layerOzone depletionGreenhouse gasProduction (economics)Consumption (sociology)Global warmingMontreal ProtocolClimate change

Abstract

fetched live from OpenAlex

This article concludes the special issue by outlining the most important indicators of the ozone regime’s success as well as issues that could slow or even prevent the complete restoration of the Earth’s protective “ozone layer” or lead to new causes of depletion in the future. Evidence for the ozone regime’s success includes the following: the declining production and consumption of ozone-depleting substance (ODS) chemicals; declining levels of ODS in the atmosphere; reduced depletion of stratospheric ozone; the projected recovery of the ozone layer during this century; reduced UV radiation and the associated environmental, human health, and economic benefits; universal participation in the regime’s treaties; the operation of regime institutions; and the regime’s ancillary success in reducing certain greenhouse gas emissions. Despite these historic successes, global ozone policy faces important challenges. These include the following: the millions of tons of ODS that remain in existing and discarded equipment and materials; the potential difficulty of completing the hydrochlorofluorocarbon (HCFC) phaseout; the broad exemptions that allow for the continued use of methyl bromide; the potential for illegal production and trade; the possibility that new ODS not covered by the regime have or will emerge; and the impacts of climate change.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.580
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.041
GPT teacher head0.264
Teacher spread0.223 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueDigitalCommons - Fairfield (Fairfield University)Same topicAtmospheric Ozone and ClimateFrench-language works237,207