Intercomparison of timothy models in northern countries
Bibliographic record
Abstract
Forage-based livestock and dairy production are the economic backbone of agriculture in many northern countries. In northern Europe and eastern Canada, forage grasses are commonly grown intensively for silage and hay as a part of crop rotation. In those regions, timothy (Phleum pratense L.) is one of the most widely grown grass species. Models that simulate the development of yield and nutritive quality have been developed for timothy, but the performance of different models has not been compared so far. In this study, we compare the performance of the models BASGRA, CATIMO, and STICS for the predictions of timothy yield at 7 sites located in Finland, Norway, Sweden, and Canada. In addition to yield, model predictions of additional variables, such as leaf area index, specific leaf area, and nutritive quality are gathered on a daily basis. Observed data will be used for two distinct calibrations: 1) Cultivar-specific and 2) ”global”, using all cultivars. The performance of the models will be tested by simulating all sites and years with both the 5 cultivar-specific parameter sets and the global parameter set. The first results of the comparison will be presented with a particular emphasis on dry matter yield predictions. The results will provide information about the uncertainties related to yield predictions of different timothy models and calibrations, the strengths and weaknesses of different modelling approaches, and the sensitivity of models to cultivar-specific parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".