Dealing with HFCs under the Montreal protocol while introducing low-GWP alternatives.
Bibliographic record
Abstract
This paper first presents specific issues that have been dealt with in Montreal Protocol HFC discussions. It briefly describes the essential elements of the amendment proposals for controlling HFC demand, such as baseline and phase-down control steps. It also summarizes the essential elements of Task Force studies as done under the Technology and Economic Assessment Panel (TEAP) during the period 2012-2015, with an emphasis on the development and commercialization of low-GWP alternatives for the various R/AC subsectors. The issue is that availability of low-GWP alternatives would enable a full discussion on HFC control schedules. An interesting analysis is how low-GWP alternatives have entered the market without that concrete schedules were in place, but by the mere expectation that certain conversions would have to happen. What will be decided on HFCs under the Montreal Protocol up to October 2016 will be after that the GL 2016 has taken place.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".