Rapid decline in hemp germination under high humidity
Bibliographic record
Abstract
Seed germination is critical for crop establishment, but storage conditions can significantly alter germination success. Industrial hemp ( Cannabis sativ a L.) has been valuable for fiber, grain, and medicinal uses since its domestication in central Asia. Despite this, modern agronomic studies, particularly on seed germination, have been limited due to legal restrictions. We assessed 21 hemp cultigens (cultivated plant types, both registered cultivars and unregistered breeding lines) bred for grain, fiber, or dual-purpose uses, evaluating the influence of seed provenance and functional type on germination rates. We compared commercially sourced seeds with feral seeds collected in Wisconsin, United States. Germination rates declined markedly from 57% in year one to 12% in year two for commercial seeds, likely due to high humidity in storage. Significant variation occurred among cultigens, with grain-type seeds generally outperforming fiber-type seeds. Seed provenance influenced germination, with seeds from Canada exhibiting higher rates than those from Italy, Poland, and Serbia; Chinese cultigens showed variable performance across years, likely reflecting genetic adaptation and handling practices. Feral seeds, initially less viable, demonstrated comparable performance to commercial seeds in long-term trials, highlighting their potential value for breeding programs. Our study underscores the importance of optimal seed storage conditions and selecting high-germination cultigens for effective hemp cultivation. Enhanced storage practices and the inclusion of feral genetics could significantly improve seed viability and crop yield, aiding the development of robust hemp production systems.
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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 | 0.001 |
| 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".