The use of seasonal accumulation of natural cold in modern air conditioning as a technology to reduce greenhouse gas emissions
Bibliographic record
Abstract
Air conditioning systems are one of the main consumers of electric energy during the warmer months. Natural cold has been used for indoor air conditioning since ancient times. The possibility of harvesting snow and using accumulated cold for various purposes during the warm season is being studied in a number of countries, such as the USA, Canada, Japan, Sweden, Norway, China. The purpose of the article is to review the existing natural sources of cold for air conditioning systems, their classification and analysis of the reduction of CO2 emissions when using natural cold for an airport air conditioning system. There are two main types of natural sources of cold: permanent action and accumulators of natural cold. The classification of air conditioning systems with seasonal accumulation of ice or snow, methods of insulation of open snow storage facilities are considered. The calculation of the reduction of CO2 emissions was performed when using an open-type cold storage facility as a source of cold for the fan coil system at the Yuzhno-Sakhalinsk airport. The reduction in annual emissions is up to 61 tons of CO2 per year, with an installed cooling system capacity of 157.4 kW or 0.39 tons per 1 kW of power. Thus, seasonal accumulation of snow or ice is a technology that makes it possible to reduce energy consumption and reduce greenhouse gas emissions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".