Bibliographic record
Abstract
China intends to achieve the goal of eliminating 35 percent of hydrochlorofluorocarbons (HCFC) in the thirteenth five-year plan (2016–20) and has been making the following efforts: (1) reinforcing the implementing ozone-depleting substance (ODS) regulations; (2) implementing the ODS phasing-out plan and strictly controlling ODS construction projects; (3) promoting the development and application of substitute environment-friendly technologies and issuing an HCFC Substitute Technology List; (4) emphasizing international environmental co-operation. The Stage 2 program of the HCFC phasing-out conference was held on 3 July for building up international co-operation for implementation. The conference introduced Stage 1 of the HCFC phasing-out plan and analyzed the situation of the HCFC phasing-out plan for Stage 2. The international seminar on green cooling/heating and energy conservation was held in Beijing and was hosted by the Ministry of Environmental Protection (MEP) and the United Nations Environmental Programme. The seminar introduced the goals, policy, and technology of ozone layer protection, air pollution prevention, and fighting climate change in China, and discussed capacity for emission reduction in relative industries.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.178 | 0.065 |
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".