Development of a full-cycle water remediation process
Bibliographic record
Abstract
Abstract A full-cycle water remediation process has been developed by expanding the capacity of an existing water treatment technology that uses combined ozonation and ultrafiltration membrane processes. The developed water reclamation process treated the effluent of a full-scale wastewater treatment plant in Canada that uses biological treatment processes to treat municipal wastewater, and reduced the colour, turbidity, suspended solids, iron and pathogen content of the effluent. The removal of hardness from the wastewater effluent was accomplished by the precipitation process. The use of lime (0.2 g/L) in the presence of NaOH operating at pH 11 showed the best results, reducing the water hardness by 89.1%. The advanced treatment capability of ozonation (8–10% w/w) and polyvinylidene fluoride (PVDF) hollow fiber ultrafiltration (UF) membrane produced a reliable source of water for municipal, industrial and agricultural use. The developed process offers important environmental benefits by reducing the diversion of water from sensitive ecosystems, decreasing wastewater discharge and preventing pollution.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".