The potential use of reed canarygrass ( <i>Phalaris arundinacea</i> L.) as a biofuel crop
Bibliographic record
Abstract
The pressures of a growing global economy, along with dwindling energy supplies and environmental concerns, especially climate change, have increased interest in the cultivation of bioenergy crops. Perennial herbaceous grasses can play an important role in this capacity. They contribute a number of desirable attributes to cropping systems such as limiting soil erosion, improving water quality, having lower agricultural chemical and nutrient requirements, and increasing the organic matter content of the soil. In this review the properties of reed canarygrass (Phalaris arundinacea L.), one of the highest-yielding cool-season grasses, are discussed with regard to its potential use as a biofuel. Higher yields of reed canarygrass are attainable since more productive accessions have been identified in breeding programs. Furthermore, biofuel quality may be improved through the selection of genotypes with relatively high cell-wall content. However, the high ash content of reed canarygrass remains a challenge, as does its invasiveness as a weed in certain regions of North America. Strategies that can be employed to lower the ash content of reed canarygrass are considered.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".