An Investigation on a Theoretical Model for Predicting a Cassava Peeler’s Useful Flesh Recovery
Bibliographic record
Abstract
This study presents a predictive model for estimating the useful flesh recovery of cassava during the peeling process.By employing a dimensional analysis approach, the model identifies key factors that influence the effectiveness of cassava peelers.Our findings demonstrate a strong correlation between experimental and predicted results, achieving a coefficient of determination (R 2 ) of 0.9754 with a Root Mean Square Error (RMSE) of 0.01, indicating high accuracy.The model highlights several optimal variables, such as tuber size, peel thickness, and friction coefficients, which significantly impact the recovery of useful flesh.These insights are crucial for improving the design and efficiency of cassava peeling machines, ultimately reducing waste and enhancing productivity in cassava processing.Incorporating this model into engineering practices allows designers to optimize peeling performance, paving the way for more efficient cassava processing solutions.This research underscores the importance of predictive modeling in agricultural engineering, providing valuable tools for enhancing food production systems.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".