Tasmania Hemp (Cannabis) Fiber and Seed Cultivar Field Trials—2018-2019
Bibliographic record
Abstract
Tasmania has a temperate climate well-suited for hemp production, is located nearer to the south pole than other regions of Australia, and therefore presents an important location for hemp variety trials.The primary aim of this research is to evaluate the performance of hemp (Cannabis) fiber and seed cultivars from various geographical origins and latitudes when grown in southern Tasmania.Seventeen registered hemp cultivars were sown on three dates in large replicate trial blocks to allow combine harvesting and collection of accurate yield data. Results are discussed with respect to site selection, crop performance, sowing dates, sowing rates, crop development, pest infestation, THC levels, seed yields, crop management and cultivar selection.Canadian cultivars performed well for grain seed production with the highest yields and their short crop height accommodates mechanical harvesting.French cultivars yielded much less seed than Canadian cultivars and grew to heights nearly beyond the reach of a standard grain header.Chinese cultivars flowered too late to produce viable seeds and are not suitable for hemp seed grain production in Tasmania, although their vigorous growth and late flowering makes them good candidates for biomass production.THC levels in the sampled Chinese cultivars were too high for seed production, but those that do not flower at Tasmanian latitudes are suitable for fiber and biomass production.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".