Predicting forage nutritive value from height and maturity of alfalfa in Saskatchewan, Canada
Bibliographic record
Abstract
Several authors have shown that fiber levels can be predicted from plant height and maximum maturity in alfalfa (Medicago satvia L.). These estimates have been used to predict animal performance without any reference to error terms. This study evaluates the equations for predicting chemical characteristics from field measurements of plant morphology, and some equations for predicting animal performance from chemical characteristics. Finally, predicting forage utilization directly from field measurements of plant morphology was evaluated. Six sites were chosen from irrigated alfalfa fields in southwestern Saskatchewan. The chemical characteristics measured were neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), acid detergent lignin (ADL), ash, acid detergent insoluble nitrogen (ADIN), neutral detergent insoluble nitrogen (NDIN), and ether extract (EE). Only ADF and NDF showed predictive value from height and maximum maturity (R2 = 0.86, and R2 = 0.90, respectively). Weiss developed a theoretical model for estimating net energy based on summing the true digestibility of each of the components. This model did not predict digestibility well (R2 = 0.23). A model was developed to predict in-vitro dry matter digestibility directly from height and maximum maturity, however this model only performed moderately well (R2 = 0.61). This shows that in-vitro digestibility is predictable directly from height and maturity, although not without significant increases in error compared to prediction of ADF and NDF. Caution would be advised when using these estimates for further prediction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".