A Wheat Grazing Model for Simulating Grain and Beef Production: Part II—Model Validation
Bibliographic record
Abstract
Computer models must be thoroughly evaluated before being used for decision‐making. The objective of this paper is to evaluate the ability of a newly developed wheat grazing model to predict fall–winter forage and winter wheat (Triticum aestivum L.) grain yield as well as daily weight gains of steer (Bos taurus) grazing on wheat pasture in Oklahoma. Experimental data of three independent field studies were used. The first was a variety trial in which fall–winter forage and grain yields were harvested. The second was a planting date experiment in which forage in the fall–winter period and grain yields were harvested. The third was a steer grazing experiment in which standing wheat biomass and steer weight gain were monitored. For the variety trials, the model efficiency (ME), which reflects how well model predictions match measured data (1 means a perfect match), was 0.102 for fall–winter forage prediction and 0.367 for grain yield. For the planting date experiment, the ME was 0.615 for predicting fall–winter forage yields and 0.409 for grain yields when a root downward extension rate of 20 mm d−1 was used. In the steer grazing experiment, the relationship between average daily weight gain and forage allowance was adequately represented by the model. For the total steer weight gains in a wide range of stocking rates and grazing durations, the ME was 0.616. Overall results show that the model, if well calibrated, has the potential to predict fall–winter forage and grain yields as well as mean daily weight gain per steer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".