Defining relative yield for soil test correlation and calibration trials in the Fertilizer Recommendation Support Tool
Bibliographic record
Abstract
Abstract The Fertilizer Recommendation Support Tool (FRST) will perform correlations between soil nutrient concentrations and crop response to fertilization from user‐selected datasets in the FRST national database. Yield response for the nutrient of interest in a particular site‐year is presented as relative yield (RY), a ratio of unfertilized yield to the maximum attainable yield ( A ). Several methods exist in the literature for estimating A and calculating RY but the effect of method choice on soil test correlation outcomes is undocumented. We used six published methods to calculate RY from site‐year yield data for five published correlation datasets, and fit a generalized linear plateau (LP) model to each. The critical soil test value (at the LP join point) and RY intercept coefficients were not significantly affected by RY method for any of the datasets, and RY plateau was significantly affected by method for only one dataset. The top options after robust group discussions were the so‐called MAX and FITMAX methods. We selected the MAX method, which defines A as the numerically highest treatment yield mean, as the most appropriate method for FRST because MAX represents maximal yield in responsive sites, is inclusive of trial data having a range of treatment numbers, limits RY to 100% (which allows options for transforming data), and is simpler to implement than FITMAX, which requires a decision tree to calculate RY for diverse trials.
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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 | 0.043 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".