Diagnosing Maize Growth for Determination of Optimum Fertilizer Application Time in Northern Malawi
Bibliographic record
Abstract
<p>Plant diagnosis was proven to be useful for predicting maize growth condition. The number of days to male flowering and that to silk emergence differed among the four sites, reflecting differences in growth condition. Maize stalk weight decreased when the number of days to male flowering exceeded 65. Two growth indexes (GIs), plant height and stem diameter, well predicted maize fresh weight as expressed by the equation: Fresh weight = -0.013 + 1.26 × (Plant height × Stem diameter) (r<sup>2</sup> = 0.57). Those two parameters changed with the growth stage according to the maize growth condition. GI monitoring revealed that week 4 (W4) or week 8 (W8) after germination was the determinant of overall maize growth. Leaf color also changed in the course of growth; leaf color at W4 was the best indicator of maize grain yield. Such plant diagnosis parameters as GI and leaf color are useful for the determination of optimum fertilizer application time.</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 | 0.001 | 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 | 0.001 |
| 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 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".