Replicability of nitrogen recommendations from ramped calibration strips in winter wheat
Bibliographic record
Abstract
Ramped calibration strips have been suggested as a way for grain producers to determine nitrogen needs more accurately. The strips use incrementally increasing levels of nitrogen and enable producers to conduct an experiment in each field to determine nitrogen needs. This study determines whether predictions from the program Ramp Analyzer 1.2 are replicable in Oklahoma hard red winter wheat (Triticum aestivum). Predictions are derived from 36 individual strips from on-farm experiments—two pairs of adjacent strips at each of nine winter wheat fields in Canadian County, OK. The two pairs of strips within each field were between 120 and 155 m apart. Each strip was analyzed three times during the 2006–2007 growing season. Nitrogen recommendations from Ramp Analyzer 1.2 are not\ncorrelated even for strips that were placed side by side, and recommendations from strips in the same field show no more homogeneity than randomly selected strips throughout the county. The results indicate that ramped calibration strips are unlikely to produce accurate nitrogen requirement predictions at any spatial scale, whether at the county level or for subsections of a single field. In contrast, a procedure that uses only measures from the plot\nwith no nitrogen and the plot with the highest level of nitrogen applied does show replicability. Thus, improvements in the ramped calibration strip technology are needed if it is to become viable.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".