Bibliographic record
Abstract
There has been a recurring interest in using Forward Osmosis (FO) process in water treatment and desalination. Despite the promising results from pilot and bench scale experiments the technology is still not commercialized yet. This is due to the complicated nature of the process which usually involves multiple stages of treatment in addition to the FO membrane process. Unfortunately, most of the recent studies were focused on studying the FO process alone and didn’t provide enough data about the actual cost of the process as whole which includes the osmotic agent regeneration stage/s. This issue resulted in some uncertainties about the total cost of the water treatment by the process. Furthermore, more data are required to evaluate the impact of the osmotic agent losses on the overall cost and efficiency. In case if the draw solution is regenerated by membrane treatment, a suitable membrane should be selected to ensure an optimal salt rejection. For power generation by Pressure Retarded Osmosis (PRO) process, there was an evident progress. However, the process is site specific; i.e. it is dependent of the availability of the draw and donor solution. This suggested that the process is applicable to certain areas but can’t be generalized.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".