Predicting fruit and vegetable processing wash-water quality
Bibliographic record
Abstract
Abstract Wastewaters from the fresh produce processing industry are high in solids and organic matter requiring adequate treatment prior to disposal or recycling. Characterization of the processing wastewater, also referred to as wash-water is challenging, as the quality is a function of the produce. Analysis of water quality parameters, such as total suspended solids, total solids, total dissolved solids, chemical oxygen demand, biochemical oxygen demand, total nitrogen, total phosphorus, ammonia, and electrical conductivity from different fruit and vegetable operations were analyzed to develop the innovative power function models and ranking system to estimate wash-water quality. The developed models take the form of Y = a(x)b, where Y, a, x, and b are estimate, scale, rank, and location parameters, respectively. The location and rank range from −0.65 to −3.18 and 0.05 (worst water quality) to 1, respectively, while the scale parameters are highly variable. Average and standard deviation estimation models show a very good fit for washing only (R2 > 73%) and washing with processing (R2 > 79%). The models and ranks highlight the degree of treatment required to address protection of surface and ground water and make the water quality conform to regulatory standards, benefiting watershed managers, government agencies, consultants, farmers, producers, processors and technology providers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".