Universal Design to Limit Food Cross-Contamination: Incased Set of Kitchen Utensils with Five Color-Coded Food Chopping Boards and Knifes
Bibliographic record
Abstract
This-study is focused on a-conceptual-design of a-kitchen-appliances/utensils-set, comprising of five-color-coded food-chopping-boards and five-matching-color-coded-knifes, to-reduce cross-contamination at private-homes, as-well-as at food-establishments. Selected-relevant-Patents, as-well-as products, available locally and internationally, were analyzed. The-Joseph-Joseph Index™ Color-Coded Chopping-Boards set (of four), was chosen as a-point of reference for the-current-design; its-seven-identified-limitations have-directed the-scope of the-current-design. Target-specifications/objectives, of the-set, were formulated from the-document-analysis, while Pair-wise-Comparison-Charts were-used, to-rank the-importance of the-objectives, in the-different-levels. The-best-ranked-design (out of the-four-alternatives made) was chosen, via standard Engineering-Design Weighted-Decision-Matrix (EDWDM) and ‘ Drop and Re-vote’ (D&R) method. 2D-drawings, of the-best-design-alternative, were created via computer-aided-design (CAD) AutoCAD-software 2018, while 3D-modeling, of the-set and all-its-components, was produced by Autodesk-Inventor-Version: 2016 (Build 200138000, 138). Designed-labels (positioned on each-board and each-knife, as-well-as on the-set itself, as an-inclined-panel) were introduced, to-cater for the-people with color-blindness (according to-the-fundamental-principles of the-Universal-design), and also to-avoid-confusion (as a-reminder which board is which) for all-users. The-study adopted ‘ analysis ’ method of materials-selection. The-main objectives, of the-intended-set, was used as a-guide, in-preliminary-materials-selection. This-concise-study has focused on conceptual -design only; and, hence, it-is further-recommends to: (i) carry-out a-detailed-design; (ii) select a-specific-material (out of the-group, identified by this-study); (iii) choose a-mode of fabrication of the-set; (iv) examine the-possibility of incorporating of anti-microbial-agent(s) and/or coating(s); (v) fabricate the-prototype(s); (vi) conduct explorative-use-ability-trials; and (vii) analyze the-marketing-aspect of the-final-set. This-work is potentially-beneficial-to engineering- product -design students and faculty, as-well-as to-households and food-establishments (subject-to successful-implementation). Keywords: Color blindness; Materials selection; Food safety; Joseph Joseph Index™ Color Coded Chopping Boards set. DOI : 10.7176/ISDE/10-5-05 Publication date :June 30 th 2019
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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) | 0.000 | 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".