Early Prediction of <i>Miscanthus</i> Biomass Production and Composition Based on the First Six Years of Cultivation
Bibliographic record
Abstract
ABSTRACT Miscanthus is a promising feedstock for second‐generation bioethanol production. This perennial crop produces its biomass in two phases: a yield‐building phase, where the biomass production increases gradually, and a plateau phase, where it is maintained. However, to target the breeding of Miscanthus for second‐generation bioethanol production, the early selection of interesting traits is critical. We therefore investigated the interannual correlations within and among the traits related to biomass production and composition. We studied 21 clones belonging to M. × giganteus J. M. Greef & Deuter ex Hodk. & Renvoize, M. sacchariflorus (Maxim.) Benth. & Hook. f. ex Franch., and M. sinensis Andersson species cultivated on plots from the second to the sixth year at two harvest dates. The biomass production, canopy height, plant stem number, and aboveground plant volume index were better predicted from the third year than from the second year (minimum correlation coefficients of 0.76 and 0.67 respectively). The stem diameter was well predicted from the second year (correlations above 0.93). The canopy height and the aboveground plant volume index determined in the second and third year were the best predictors of the biomass produced in the second, third, and fourth year (minimum correlations of 0.77 against 0.52 for flowering date or 0.64 for stem diameter). For older crops, the canopy height measured in the second and third year was the best predictor of the biomass production (correlations above 0.70). The interannual correlations were lower for the biomass composition‐related traits than for the production‐related traits and fluctuated over time. These results showed that early prediction of interesting traits is feasible to breed varieties tailored for biofuel production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".