Bibliographic record
Abstract
Fat, oil, and grease (FOG) in wastewater can cause foul odor, sewer line blockage, and may interfere with sewage treatment. FOG control is approached with physical, chemical, and biological methods Many cities, including Edmonton, Alberta, Canada, have effectively applied commercial biological products to control FOG. Analysis of samples collected from wet wells in Edmonton was undertaken to examine the factors that influence the FOG control performance of commercial biological products. Field sampling showed a seasonal variation of FOG and COD concentrations indicating that the higher temperature in the summer-autumn term compared to the winter-spring term benefited FOG removal. The lowest FOG concentration (49.3 mg/L) was observed when the products were applied with a mixer on in summer-autumn term, which suggests the importance of oxygen and thorough mixing. Based on the results of wet well sample analyses, bench-scale experiments investigated the impacts on FOG removal of product dosage, initial COD, and temperature. Addition of 1000 times the recommended dosage of the commercial products increased FOG removal from 35.5% (achieved at the recommended dosage) to 41.1% in 14 days with an initial COD of 600 mg/L in 14 days. FOG removal increased from 29.8% to 48.0% with an increase in temperature from 15 °C to 32 °C. Suggestions to improve FOG control with commercial biological product application are proposed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".