Assessment of an indirect technique to predict hay and silage storage dry matter losses through Monte Carlo simulation
Bibliographic record
Abstract
Control of dry matter losses (DML) is a major concern of forage conservation systems. Measuring DML during hay and silage storage is difficult and time-consuming, so it is usually limited to experimental conditions. The lack of a practical way of measuring DML to monitor forage conservation efficiency has contributed to the poor adoption of good practices. The availability of a practical, easy, and economic technique capable of estimating on-farm DML would facilitate advisory and extension work. The objective of this study was to assess the accuracy and precision of an indirect technique based on compositional changes to estimate storage DML for silages and hays. Data were generated through a Monte Carlo simulation developed to test the effects of type of data distribution (normal or log-normal), variability (5 and 10% coefficient of variation), and sample size (1000, 30, 20, and 10). Results indicated that potential markers (acid detergent fibre and acid detergent lignin were explored) had log-normal distribution and that a coefficient of variation of ~10% was reasonable. Summary statistic analysis showed that means and medians were coherent for different sample sizes. It was concluded that changes in marker concentrations could lead to a reasonably robust system of predicting DML during hay or silage storage.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".