Effect of Soil Moisture Regimes on Seed Iron and Zinc Concentration of Biofortified Bean Genotypes against Malnutrition in Sud-Kivu Highlands
Bibliographic record
Abstract
This study investigated the influence of three soil moisture irrigation regimes on concentration of seed iron and zinc content of four biofortified bean varieties promoted for eradication of malnutrition in Sud-Kivu highlands. A field experiment was conducted in the Hogola marsh highlands during two cultural seasons B2013 and B2014. The experiment design was a RCBD with a split plot arrangement where the main plots were 110 m2 and split plots 20 m2. A strategic application of homogenisation of the experimental site’s soil fertility by chemical fertilizers of the type: CaCO3, KCl and DAP was conducted out. Four biofortified varieties (CODMLB001, RWR2245, HM21-7 and RWK10) constituted main factor, while water regimes respectively [bottom of the slope: R1 = 48% soil moisture, at the middle of the slope R2 = 37% soil moisture and at the top of the slope: R3 = 29% soil moisture according to the gradient of humidity] represented secondary factor and seasonality, tertiary factor. The study showed that the concentrations of iron and zinc were highly correlated with soil moisture regimes. The variety HM21-7 demonstrated better adaptability because it showed a low rate of reduction of iron and zinc concentration under the three soil moisture regimes and was therefore best suited to fight malnutrition in the Sud-Kivu province.
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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 | 0.000 |
| 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 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".