Cultivation of low tetrahydrocannabinol (THC) Cannabis sativa L. cultivation in Victoria, Australia: Do we know enough?
Bibliographic record
Abstract
Late 2017, the ban on the cultivation and consumption of low tetrahydrocannabinol (THC) Cannabis sativa L. in Victoria, was lifted by the Federal Government of Australia. Its legalization presents the opportunity for Victoria to become a leading producer and distributer of these economically valuable hemp products. However, as a novel crop to Victoria, there is little information available for obtaining economically viable yields. Therefore, the objectives of this review were to firstly, develop an understanding of the environmental requirements shared by C. sativa cultivars, and what conditions promote fibre and grain yields. Secondly, it seeks to identify what farming practices have been conducted throughout Europe, Canada and China, and to explore whether these practices could be adapted to Victoria. Thirdly, the review will assist in making recommendations regarding which cultivars would be ‘potential’ candidates for commencing trials under Victorian climates so to find out the varieties that can provide high yields for fibre, grain and dual-purpose production. This review notes that Victoria shares a similar climate to central Europe, and has an ideal climate for the development of a successful hemp industry, as it has suitable lengths of daylight throughout spring and summer months and meets the precipitation requirements. This review has thus strongly suggested that the properties and attributes of European varieties of C. sativa should be further researched for site-specific cultivation in Victoria for fibre, grain and dual-purpose production in order to maximise harvest yields.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".