Irrigation Management Strategies for Medical Cannabis in Controlled Environments
Bibliographic record
Abstract
Medical cannabis production is a new industry in Canada and represents a challenge for the production of a repeatable and standardized product for medical use. A reliable and reproducible environmental control strategy can contribute significantly to meeting this challenge. Irrigation management and control of plant water status is one of the key environmental control elements. To assess the effects of various irrigation management strategies this study deployed in situ stem psychrometers to measure the water status of plants. As a routine feedback device for irrigation control these devices are not ideal for large-scale production so correlation with the key environment variable representing the aerial demand for moisture (vapour pressure deficit) was assessed. By establishing a relationship between cumulative water potential (cWP) and cumulative vapour pressure deficit (cVPD) an irrigation management strategy that predicted plant water status based on measurements of cVPD could be employed. Three treatments; control (irrigation events every 1-2 days), mild-stress (irrigation events every 2 days), and moderate-stress (irrigation events every 3 days) were tested. The effects of flushing were also investigated to determine whether it had the intended effect of reducing nutrient concentrations within the dried bud. Through the use of psychrometers, water status (cWP) thresholds were correlated with humidity (cVPD) thresholds and reduced irrigation frequency resulting in water use reductions up to 45.7% which had negligible impacts on yield and cannabinoid profile. Flushing was found to be ineffective in removing any significant amount of nutrient from the bud.
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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".