Manual dishwashing process – a pre‐assigned behaviour?
Bibliographic record
Abstract
Abstract Global studies have observed many techniques of manual dishwashing causing different levels of performance and using quite different amounts of water, energy, time and detergent. It is not known, however, if these techniques are pre‐assigned to a person and persist when dishes are washed under different conditions, or are adapted to the specific type of dish‐cleaning process. Here we explored this question in a study with 40 test subjects selected equally from Germany and East European countries by asking them to wash two place settings of dishes with different amounts of soil three times. The results showed that the test subjects did not adapt their washing‐up behaviour to the amount of soil. In general, no significant differences were found in the water, energy and detergent consumption for all test subjects. Only the time used by the German test subjects to wash the fully soiled dishes was significantly longer compared with the dishes with only a quarter the quantity of soil, and no significant difference was observed for all other parts. The only significant difference found between the level of soiling of the dishes was the cleaning result achieved: The less soiled the dishes were, the better the final cleaning result was, and this related to all test subjects. This lends support to the proposition that the consumers did not adapt their washing‐up behaviour to the specific circumstances of the dishwashing job to be done but retained some pre‐assigned behaviour.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".