Selecting the High‐Yield Subpopulation for Diagnosing Nutrient Imbalance in Crops
Bibliographic record
Abstract
Plant nutrient status is currently diagnosed using empirically derived nutrient norms from an arbitrarily defined high‐yield subpopulation above a quantitative yield target. Generic models can assist Compositional Nutrient Diagnosis (CND) in providing a yield cutoff value between low‐ and high‐yield subpopulations for small databases. Our objective was to compute the minimum yield target for sweet corn (Zea mays L.) and the corresponding critical CND nutrient imbalance index using a cumulative variance ratio function and the chi‐square distribution function. Population (40 observations) and validation (20 observations) data were selected at random from a survey database of 240 observations including commercial yields and leaf nutrient concentrations. A filling value (Rd) was computed as the difference between 100% and the sum of d nutrient proportions [Rd = 100 − (N + P + K + …)]. The CND nutrient expressions were the row‐centered ratios of N, P, and Rd proportions in tissue specimens. Variance ratio computations of CND nutrient expressions among two subpopulations arranged in a decreasing yield order were iterated across population data. The proportion of low‐yield subpopulation computed at the inflection point of a cubic cumulative variance ratio function was 67.5%, the minimum proportion of low‐yield specimens. That exact probability corresponded to a theoretical chi‐square value (CND r2) of 1.5 for three components. The critical CND r2 value was validated using independent samples and the sum of the squared CND nutrient indices. The procedure is applicable to small‐size crop nutrient databases for solving nutrient imbalance problems in specific agroecosystems. A calculation example is presented.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".