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Record W4367023815 · doi:10.1021/cen-10035-polcon2

US Senate approves HFC treaty

2022· article· en· W4367023815 on OpenAlexaboutno aff
CHERYL HOGUE

Bibliographic record

VenueC&EN Global Enterprise · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal ProtocolTreatyPolitical scienceLawGovernment (linguistics)Agency (philosophy)AmendmentGreenhouse gasBusinessPublic administrationOzone layerGeographyMeteorologySociology

Abstract

fetched live from OpenAlex

The Senate voted 69–27 on Sept. 21 to allow the US to join an international treaty to curb the production and use of hydrofluorocarbons (HFCs). The chemicals are industrial gases used in a slew of applications, including as refrigerants in air conditioners and freezers. They are potent greenhouse gases. In late 2020, Congress passed a law authorizing the federal government to meet the terms of the pact , the 2016 Kigali amendment to the Montreal Protocol. Using that law, the Environmental Protection Agency issued a regulation last year to reduce allowable US production and use of HFCs to 15% of 2011–13 average levels by 2036. Such reduction will meet the terms of the Kigali amendment. But because the Senate had not ratified the Kigali deal, the US could not participate as a full treaty partner in international talks related to it. Almost a year ago, President Joe Biden asked the

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.235
Teacher spread0.202 · 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; both teacher heads agree on what is shown here.

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
Published2022
Admission routes1
Has abstractyes

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