Bibliographic record
Abstract
Desalination applications based on reverse osmosis (RO) technology today comprise over 50% of the capacity of all desalination systems worldwide and represent 75-85% of new desalination projects being implemented. The major reason for the shift in desalination projects to RO technology is the high energy efficiency of the RO process. There are three major application categories of large capacity, RO-based desalination projects: brackish RO; advanced municipal wastewater reclamation; and seawater RO. In the two first categories (brackish RO and wastewater reclamation), the systems’ configuration and equipment components are well defined. Therefore, project costs and operating expenses are fairly predictable. In seawater RO desalination systems, the RO process configuration is also very similar; however, some variability exists regarding the configuration of seawater water delivery and feed water pretreatment. The rest of the system’s components and system operation methods are very uniform. However, an evaluation of published cost data of medium- to large-scale water RO desalination projects illustrates significant variability in costs of desalination systems. This paper will analyze current economic conditions of seawater desalination, and highlight the limitations and possibilities of additional improvements of the economics of the SWRO desalination process.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".