Assessment of Preparation Methods to Produce a Postharvest Spinach Wash Water Model for Sanitizer Validation Studies and Comparison of Sanitizer Quantitation Methods
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Currently no standard benchtop preparation method exists for simulated produce wash water, which makes it challenging to compare sanitizer efficacy reports and provide guidance for growers regarding water quality monitoring and free chlorine quantification. This work compares benchtop preparation methods for spinach-based model wash water (blender vs stomacher), metrics for organic load standardization (chemical oxygen demand (COD) vs nephelometric turbidity units (NTU)), and free chlorine quantitation methods ( N, N -diethyl- p -phenylenediamine (DPD) vs iodometric titration (IOD)). It was found that COD is a more reliable metric for organic load standardization than NTU. Blender- and stomacher-generated wash water had similar physicochemical properties at organic loads up to 1000 mg/L COD, so both methods are acceptable, and DPD titration reflected expected patterns of free chlorine consumption in wash water more accurately than IOD. These results support the use of select wash water preparation and free chlorine detection methods, informing the development of a standardized protocol.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".