Bibliographic record
Abstract
Abstract Visible plumes above wet cooling towers are of great concern due to the associated aesthetic and environmental impacts. The parallel path wet/dry cooling tower is one of the most commonly used approaches for plume abatement, however, the associated capital cost is usually high due to the addition of the dry coils. Recently, passive technologies, which make use of free solar energy or the latent heat of the hot, moist air rising through the cooling tower fill, have been proposed to minimize or abate the visible plume and/or conserve water. In this review, we contrast established versus novel technologies and give a perspective on the relative merits and demerits of each. Of course, no assessment of the severity of a visible plume can be made without first understanding its atmospheric trajectory. To this end, numerous attempts, being either theoretical or numerical or experimental, have been proposed to predict plume behavior in atmospheres that are either uniform versus density-stratified or still versus windy (whether highly-turbulent or not). Problems of particular interests are plume rise/deflection, condensation and drift deposition, the latter consideration being a concern of public health due to the possible transport and spread of Legionella bacteria.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".