Growth and Yield of Kale, Swiss chard, Amaranth, and Arugula microgreens in response to different growing medium substrates
Bibliographic record
Abstract
Microgreens are relatively novel food with high nutrition and dietary benefits that can be influenced by the growing medium. Two experiments were performed to develop an optimum media for microgreensfrom kale (Brassica oleracea L. var. acephala), Swiss chard (Betavulgaris var. cicla), arugula (Eruca vesicaria ssp. sativa), and amaranth (Amaranthus tricolour L.). Experiment 1 was screening of media T1 = 30% vermicast + 40% sawdust + 30% perlite; T2 = 30% vermicast + 50% sawdust + 20% perlite; T3 = 50% vermicast + 30% sawdust + 20% perlite; T4 = 30% vermicast + 40% sawdust + 30% mushroom compost; T5 = 30% vermicast + 20% sawdust + 20% perlite + 30% mushroom compost; and a negative control (NC) = 50% sawdust + 50% mushroom compost. The positive control was Promix BX™ potting mix alone. Experiment 2 was to test the efficacy of two different sources of mushroom compost (White oyster mushroom compost (MC1) and Shiitake mushroom compost (MC2)) added to media T1 to T5 above. The results showed that the media physicochemical properties varied across treatments. Higher chemical parameters were obtained for T4, T5, and media containing MC1. Porosity and water retention were increased in media containing MC2 compared to the other.Seed germination, plant height, and microgreen yield were statistically (P>0.05) enhanced by T2 and T4 that contained MC2 compared to the rest. Microgreens yield was approximately three times higher in T2 and 4 with added MC2, except the yield of arugula which was two times higher in these media compared to the control. Overall, T5 alone, and T2 and T4 with added MC2 were the most effective media for microgreen production. Future studies will assess microgreens’ nutrients in different media.
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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 | 0.000 | 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) | 0.000 | 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".