Bibliographic record
Abstract
South Africa is committed to preserving the environment and addressing climate change related issues based on science and equity. In 2019, South Africa ratified the Kigali Amendment to the Montreal Protocol to reduce the consumption and production of hydrofluorocarbons (HFCs) to simultaneously protect the ozone layer and contribute to mitigating climate change. In 2015, South Africa also signed the United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement to fight against climate change and committed to achieve a “peak, plateau and decline” greenhouse gas (GHG) trajectory at a level between 398 and 614 MtCO2e/year by 20301. In 2021 revised target ranges of 398-510 Mt CO2-eq for 2025, and 398-440 Mt CO2-eq for 2030 were issued, as well as aspiring to reach a net zero carbon economy by 2050.\nAddressing the environmental impacts of cooling products converges the objectives of these two treaties. Cooling products are the main source of HFC use and they consume a significant amount of electricity produced from emission intensive coal fired power plants. South Africa’s efforts to mitigate global warming can therefore be amplified if the energy efficiency (EE) of cooling products is improved at the same time a refrigerant transition from HFC is considered. Synergistic actions with respect to sustainable cooling access across sectors will have a higher impact than actions taken in isolation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.013 |
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".