Effect of Storage Methods on Carbohydrate and Moisture of Cassava Planting Materials
Bibliographic record
Abstract
Storage of cassava (Manihot esculenta Cruntz.) planting materials has been a challenge because of its properties of moisture and carbohydrates loss under storage. Two varieties of cassava cuttings 1 m long, stored for four months under four different storage methods in two locations Kabete and Kiboko. The storage methods were clamp under double shade (CUDS), horizontal under shade (HUS), vertical under shade (VUS) and the control horizontal under open ground (HOUG). In each storage method data loggers were installed to record temperature and RH. Percentage carbohydrate, moisture content (MC), 100% dry cuttings (DC) and cuttings dried to 25% or more of its stored length but not 100% were measured at intervals of 4 weeks. Data were subjected ANOVA and means separated using LSD. CUDS performed better than other storage methods in all parameters measured. The results showed cuttings stored under CUDS lost less moisture than those stored in HUOG. The moisture loss in CUDS was from 70.16%-56.69% while that of HUOG dropped from 70.16% to 27.26% within 8 weeks after storage. High rate of carbohydrate loss was observed in Kiboko than Kabete. Mean temperatures were 25 °C Kiboko and 22 °C Kabete. The results showed that temperature had effect on loss of carbohydrate. The results have proven that safe storage of cassava planting material is affected by plant related factors as well as environmental conditions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".