Development of a National Alfalfa Quality Assessment Procedure in the United States
Bibliographic record
Abstract
Only a small fraction of the U.S. hay crop is evaluated for nutritive properties. Various states have developed alfalfa hay quality prediction systems based on chemical analysis. Confusion among users has resulted from nonuniformity among systems. Additional factors causing concern include: increased awareness among producers of higher income from the sale of high quality hay; increased demand among dairymen for a prediction system compatible with ration formulation; recognition of the inadequacy and nonuse of U.S. federal hay grades; increased movement of alfalfa hay across state borders; and lack of agreement on duplicate samples sent to different laboratories. After formation of the National Alfalfa Hay Quality Committee (NAHQC), discussion during the next one and a half years produced agreement on a procedure for determining nutritive value of alfalfa hay which inculded a sampling procedure; supplementary visual standards; laboratory procedures for acid detergent fiber (ADF), crude protein (CP), and dry matter (DM); use of near-infrared-reflectance spectroscopy (NIRS); compilation of digestion studies to predict digestible dry matter (DDM) from ADF, and the conversion of DDM to digestible energy (DE) for ration balancing; and the establishment of a laboratory certification program. The procedure has been recommended for implementation and evaluation on a nationwide basis. Canada is also evaluating the system.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".