A Comprehensive Assessment of Technical Impacts and User Experience with Food Waste Grinders in Multiunit Residential Buildings
Bibliographic record
Abstract
This study investigated the impact of food waste grinder (FWG) use on potable water consumption, wastewater characteristics, solid waste diversion, and resident attitudes in a multiunit residential building (MURB), which also provided source separated organics collection (green bins) to residents as a means to dispose of food waste. Baseline conditions were assessed during a four-month control period where residents had access to only green bins as a means to divert food waste from mixed solid waste, which was followed by an 11 month study period where residents had access to green bins and FWGs. No significant increase in potable water consumption was observed with FWG use. With the exception of fixed dissolved solids and fats, oils, and grease (FOG) (increases of 16% and 45%, respectively, though FOG was lower than typical wastewater), the generation of measured wastewater constituents did not increase significantly with FWG use. The variability of most wastewater constituent concentrations increased considerably after FWG activation, suggesting that widespread use of FWGs in MURBs may result in increased variability in the influent to wastewater treatment plants. The quantity of organics in the mixed solid waste stream did not decrease following FWG implementation, but the amount of unavoidable food waste present in the green bin stream decreased (−20%), suggesting that materials disposed of in the FWG had been disposed of in the green bin stream prior to FWG activation. Resident survey respondents indicated using both devices for food waste disposal, with no device consistently preferred by the population. To our best knowledge, this was the first study to focus specifically on the impact of FWG implementation in a MURB population, which also had access to green bins as a means of disposing of organic waste.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".