Potassium and micronutrient fertilizer addition in a mock aquaponic system for drug-type <i>Cannabis sativa</i> L. cultivation
Bibliographic record
Abstract
Cultivating drug-type Cannabis sativa L. with aquaponics could reduce mineral fertilizer use; however, its nutrient solution is often unbalanced and low in K + and micronutrients. It is unknown if a K + fertilizer, a micronutrient fertilizer, or both, would improve C. sativa production in aquaponic solution, as optimal K + and micronutrient concentrations in the root zone for C. sativa during the flowering stage have not been investigated. To determine the effects of adding a K + fertilizer and a micronutrient fertilizer to aquaponic solution for C. sativa production, we grew drug-type C. sativa in five aquaponic based solutions: aquaponic solution (control plants) (15 mg·L −1 K + ); aquaponic solution with added micronutrients (Fe 3+ , Cu 2+ , Mn 2+ , B 3+ , Mo 3+ , and Zn 2+ ); and aquaponic solution with added micronutrients and three K + concentrations (75, 113, and 150 mg·L −1 ) during the flowering stage. To evaluate the impact of additional K + and micronutrients on C. sativa production, we measured growth (vegetative parameters and weight), physiology (leaf gas exchange), leaf nutrition content, and yield (inflorescence weight). Adding the K + fertilizer at 75 and 113 mg·L −1 with micronutrients to aquaponic solution increased harvest index (marketable inflorescence to shoot weight) by 16% and 22% compared with the control, respectively. Cannabis sativa dry apical inflorescence and total inflorescence yield also increased linearly with increasing K + concentration. Alternatively, plants grown in the control (suboptimal K + and micronutrient conditions) had no difference in growth or measured physiological parameters compared with plants with supplemented nutrients. Our study suggests that aquaponic solution mitigates low K + concentrations from causing deficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".