Wastewater characterization and management in Ontario's food processing sector
Bibliographic record
Abstract
Research was undertaken to characterize raw wastewater, effluent and sludge, and assessed wastewater treatment systems in Ontario's food industry, that discharge wastewater directly to surface water or land. Samples from 48 sampling points in 12 plants were collected seasonally for a whole year and were analyzed for up to 234 parameters. About 45,000 data points were obtained. Two Access databases were built for the data management. Systematic statistical methodologies were developed for the data analysis, including box plot, dot diagram, Levene's test, and one-way ANOVA. Based on intensive data analysis, a comprehensive characterization of raw wastewater, effluent and sludge is presented. The results and corresponding explanations provide a basis for best management practices in Ontario's food processing sector. For example, the leakage of milk and milk products is the main reason for the high levels of pollutants such as TKN in dairy wastewater. Potential measures for the improvement of wastewater treatment systems of certain plants are provided. A good example is that increasing aeration could reduce the concentration of ammonia in the effluent from some plants.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".