An adapted Weibull function for agricultural applications
Why this work is in the frame
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Bibliographic record
Abstract
The Weibull function is applied extensively in the life sciences and engineering but underused in agriculture. The function was consequently adapted to include parameters and metrics that increase its utility for characterizing agricultural processes. The parameters included initial and final dependent variables (Y 0 and Y F, respectively), initial independent variable (x 0 ), a scale constant (k), and a shape constant (c). The primary metrics included mode, integral average, domain, skewness, and kurtosis. Nested within the Weibull function are the Mitscherlich and Rayleigh functions where c is fixed at 1 and 2, respectively. At least one of the three models provided an excellent fit to six example agricultural datasets, as evidenced by large adjusted coefficient of determination (R A 2 ≥ 0.9266), small normalized mean bias error (MBE N ≤ 1.49%), and small normalized standard error of regression (SER N ≤ 8.08%). The Mitscherlich function provided the most probable (P X ) representation of corn (Zea mays L.) yield (P M = 87.2%); Rayleigh was most probable for soil organic carbon depth profile (P R = 96.4%); and Weibull was most probable for corn seedling emergence (P W = 100%), nitrous oxide emissions (P W = 100%), nitrogen mineralization (P W = 58.4%), and soil water desorption (P W = 100%). The Weibull fit to the desorption data was also equivalent to those of the well-established van Genuchten and Groenevelt–Grant desorption models. It was concluded that the adapted Weibull function has good potential for widespread and informative application to agricultural data and processes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it