Bibliographic record
Abstract
Lack of cooling and cold-chain access is a critical development challenge that has significant implications for people's livelihoods, productivity, health, food, and nutritional security. While business-as-usual demand projections suggest 19 new cooling appliances will be sold every second by 2050, universal access to cooling is expected not to be a reality even at this rate of growth, leaving poor and vulnerable populations to suffer the consequences. The global demand for cooling is already pressuring the energy system and the environment and given all the social and economic benefits of cooling and cold-chain but also the environmental risks, there is now a major opportunity for governments and the private sector to develop and deploy sustainable, affordable, and resilient cooling solutions, and contribute to three internationally agreed goals simultaneously: the Paris Agreement; Sustainable Development Goals (SDGs); and the Kigali Amendment to Montreal Protocol. Achieving this will require a radically different approach to cooling and cold-chain provision that starts by asking what energy services are needed and explores ways to meet them with minimum environmental impact and cost, taking into account available renewable, thermal, and waste energy resources, synergies between processes and systems, and aggregation opportunities, rather than defaulting to electricity to generate cooling. Such a system-level approach sits at the core of the Cold Economy.
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 | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.090 | 0.029 |
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".