Effect of Moisture Content on Selected Physical and Mechanical Properties of Two Varieties of Tigernut (Cyperus spp)
Bibliographic record
Abstract
The effect of moisture content on some physical and mechanical properties of two varieties of tigernuts (Cyperus esculentus) was investigated. These properties include: geometric dimensions, linear dimensions, 1000 tuber weight, bulk density, tuber size, sphericity, angle of repose, porosity, coefficient of static friction and compressive strength. The moisture content levels used were 20, 25, 30, 35 and 40% wet basis (wb), and the two tigernut varieties used were yellow and brown types. The linear dimension, geometric diameter, sphericity, 1000- tuber weight, bulk density and angle of repose in both varieties increased with increasing moisture content. The average length, width and thickness of the yellow variety increases more than the brown variety at the determined moisture contents. True density of the yellow variety increased while the brown variety decreased with increase in moisture content. The porosity of the yellow variety reduces with increase in moisture content from 45.95 at 20% mc to 42.4 at 40% mc, while the brown variety decreased from 42.72 at 20% mc to 30.77 at 40% moisture content. The yellow variety had bigger size tubers than the brown variety and this has serious implications in packing, handling and transportation issues.
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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 | 0.000 | 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 | 0.001 |
| 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".