X-Ray Fluorescence-Analyzed Mineral Micronutrient DensityVariation Among Kenyan Local Vegetables
Bibliographic record
Abstract
<p>In Africa staple cereal foods are often eaten along with the African Leafy Vegetables (ALVs) rendering meals rich in micronutrients. To increase the mineral micronutrient value, it is imperative to intensify their cultivation given their under-utilized diversity, neglect until needed, slow rate of incorporation into crop value chains for lack clear micronutrient nutrition driven agenda. As a way of focusing on their ionomic nutraceutic attribute potential, the first objective was to investigate accession ionome differentials on the basis a soil mineral criterion.The second objective was to determine a method for variation assessment of ionomic micronutrient dense variants in key Kenyan local vegetables. Four ALVs species constituting 25 accessions were collected in short season rains of 2003 from north and souther stretch of western Kenyaand in early Long rains of 2004 from south eastern Kenya. A stratified sampling design organized had three collection points per farm; on three farms per site; and at three sites per phyto-region. ALV samples together with accompanying soils were Energy Dispersive X-ray fluorescence (XRF) analysed. The data were used, first, for ionomically differentiating populations by way of singling out respective element influenced accession differentials (SELIACDs). Second, elements were jointly resolved into multi-element influenced accession differentials (MELIACDs). Agro-edaphic effects on population ionome niching were assessed using analysis of variance and graphical aids. Geometric means were generated and awarded nutrametric merit scores to allow for nutrametric grading of accessions. Results suggest a great deal of ionomic phenotypic plasticity among the local vegetable accessions as a function at scale of farm, site and/or region soils. The SELIACD method was useful for piecemeal separation of accession on a single element basis but which method would require the development of a selection index with a certainty of a significant genetic gain at the onset. The joint MELIACD and the nutrametric grading methods are proposed as a promising basis for prebreeding tool prioritization given the XRF-analytic novelty and the emerging interest in ALV nutraceutical cropping.</p>
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".