Comparing the performance of nutritive value predictions in three timothy models
Bibliographic record
Abstract
Grasslands are the main source of energy and nutrients in ruminant production systems. Nutritive value of grasslands is in most feeding systems described based on energy, i.e. digestibility and cell wall content, and crude protein content of the feed and plays a significant role in the profitability of these production systems. Timothy (Phleum pratense L.) is a widely used forage grass grown either in pure stands or in mixtures with other forage grasses and legumes in cold-temperate regions of the world. Timothy management practices, including cultivar selection, cutting frequency, and fertilization are adapted to the climate and soil conditions as well as to the animal production system this grass is part of. Models exist that can simulate phenological development, dry matter growth, digestibility and nutritive value of timothy as a function of the weather, soil, and management practices. These models differ in how they represent plant processes related to nutritive value. An analysis of these differences is needed to identify the correct process representation, and requires comparing model outputs against data from experiments conducted under different climate, soil, and management conditions. The overall goal of this study was to compare the ability of three simulation models, BASGRA, CATIMO and STICS, to predict fibre and crude protein concentrations along with digestibility. Datasets covering a wide range of climate and soil conditions, cultivars, and management practices in major timothy grass production regions of Canada, Finland, Norway, and Sweden were used for model calibration and validation. Simulations results were then analysed to better understand the strengths and the weaknesses of the modeling approaches used in the evaluated models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".