Bibliographic record
Abstract
Mechanistic simulation models can assist in developing recommendations to optimize yield and nutritive value and in understanding the complex interaction among plant growth, nutritive value, and environmental conditions. In this paper, we present the growth and N concentration modules of an integrated model [CATIMO (Canadian Timothy Model)] of timothy (Phleum pratense L.) primary growth and nutritive value. This growth model features radiation interception and use efficiency, leaf and stem growth, leaf senescence, and a N function based on the critical N concentration of whole plants. Model parameters were calibrated to key model attributes: leaf area index (LAI); forage N concentration; and leaf, stem, and forage dry matter (DM) yields. Calibration measurements were taken weekly on timothy primary growth in four different years at one location (Fredericton, NB, Canada). Overall, the model satisfactorily fitted the measured values with root mean square errors of 32.8, 42.0, and 65.9 g m−2 leaf, stem, and forage DM yield, respectively. The model tended to underestimate stem DM yield at the end of the primary growth cycle, overestimate forage N concentration under nonlimiting N conditions, and underestimate N concentration under limiting N conditions. The model satisfactorily fitted LAI in 3 of 4 yr. Summary statistics of the calibration indicate a successful description of growth and development of the essential plant components required for modeling digestibility.
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 | 0.000 | 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) | 0.003 | 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".