Evaluation of industrial hemp yield and quality in the province of Québec
Bibliographic record
Abstract
Industrial hemp (Cannabis sativa L.) is a multipurpose crop for which there is growing interest. However, there is currently limited information on the adaptability of commercial cultivars in eastern Canada. The present project assessed the adaptability of eleven cultivars (Anka, Alyssa, CanMa, CFX-1, CFX-2, CRS-1, Delores, Férimon, Finola, Jutta, and Yvonne) in four contrasting regions of Québec, in terms of hemp seed and fiber yield and quality. Average seed and fiber yields were respectively 1315 and 3226 kg ha-1. Férimon, Jutta, Anka and CanMa showed superior and stable seed yields across the environments. Férimon stood out from the others in terms of fiber yield having the highest yields followed by Anka and Jutta. Seed crude protein (CP) concentrations varied between cultivars and averaged 237 g kg-1. Cultivars with lower agronomical yield also had higher CP concentration. Cellulose, hemicellulose and lignin concentrations of stems respectively averaged 564, 123 and 93 g kg-1. Limited variations were observed among cultivars. In addition, fertilization trials were performed with CRS-1 and Anka (N & K: 0, 50, 100, 150 and 200 kg ha-1 and P: 0, 25, 50, 75 and 100 kg ha-1). A positive linear response of seed and fiber yields and crude protein concentration was observed following nitrogen fertilization, whereas no response was observed for phosphorus and potassium.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".