Gas Infusion technology: An innovative way to oxygenate and treat wastewater
Bibliographic record
Abstract
Gas Infusion is a technology which promotes a high-rate of oxygen dissolution in aqueous streams and has been used in many processes that requires oxygen, such as aquaculture, aquifer remediation, algae bloom reduction, and beverage production, amongst others. Gas Infusion can maintain higher oxygen levels in liquids due to a proprietary gas-exchange process, allowing the introduction of pure oxygen in a bubble-less manner, creating a stable liquid stream containing enormous quantities of dissolved oxygen. The utilisation of Prosper’s Gas Infusion technology as the primary oxygen provider for a conventional activated sludge wastewater treatment is now the hypothesis to be proved. This paper evaluates Gas Infusion technology based on the results from full scale pilot plants developed for Canadian and Brazilian utilities. One of the goals of the pilots was primarily prove the efficacy of the process in terms of oxygen introduction and Biochemical Oxygen Demand (BOD) removal. Another goal was to get better understanding on how a full-scale system could be configured and the implications on capital and operational expenditures (CAPEX and OPEX, respectively) of wastewater green-field plants implementation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".