Bibliographic record
Abstract
Growth and nutritive value of forage crops have rarely been integrated into one mechanistic computer simulation model. In a companion paper, we described the growth and N concentration modules of an integrated model of timothy (Phleum pratense L.) primary growth and nutritive value, known as CATIMO (Canadian Timothy Model). In this paper, we describe the digestibility module, which features the cell wall (CW) digestibility and CW concentration for each of the plant morphological components derived from the growth module of the model. The model parameters were calibrated to key model attributes: concentration and digestibility of CW in leaves, stems, and forage and dry matter (DM) digestibility of leaves, stems, and forage. Calibration measurements were taken weekly on timothy primary growth in four different years at one location (Fredericton, NB, Canada). The model satisfactorily fitted the measured values with root mean square errors of estimation (RMSE) of 0.051 g g−1 DM for forage CW concentration, 0.026 g g−1 CW for forage CW digestibility, and 0.018 g g−1 DM for forage DM digestibility. The leaf and stem CW concentration (RMSE ≤ 0.044 g g−1 DM), CW digestibility (RMSE ≤ 0.019 g g−1 CW), and DM digestibility (RMSE ≤ 0.018 g g−1 DM) were also calculated satisfactorily by the model. The CATIMO model is a promising tool because of its mechanistic and flexible approach and the good agreement between measured and simulated values.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".