The influence of synthetic mulches to improve certified organic hardneck garlic production in the British Columbia southern interior
Bibliographic record
Abstract
Garlic (Allium sativum) is one of the most commonly used vegetables in the world and its production is continually increasing. In combination with the organic food trend, the demand for certified organic garlic is of important interest for Canadian growers. Hardneck garlic (A. sativum subsp. ophioscorodon) is well adapted for British Columbia, producing best in the continental climates of the Kootenay and Okanagan regions where distinct warm summers and cold winters commonly occur. Garlic, however, can be a challenge to produce organically as it does not compete well with weed pressure, requires relatively high amounts of soil nutrition and is grown in a biennial cropping system. Synthetic mulches have been adopted in organic production as they can be an economical method to improve vegetable production by reducing weed pressure and modifying the soil temperature and moisture. Within our research, we plan to provide initial results to improve Canadian organic garlic production by evaluating the influence of synthetic mulches, plastic and biodegradable, on soil and plant attributes. In 2017-18, we conducted a randomized complete block design experiment to compare garlic production of black plastic, white plastic and craft paper mulch treatments to a control (no mulch) at a certified organic farm in Krestova, British Columbia. In this study, we evaluated garlic characteristics associated with yield and quality, changes in soil nutrition, and weed control of the mulch treatments in a biennial cropping system. We hypothesize that garlic quality and overall yield will be improved when using synthetic mulches. The results from this experiment will help to focus future research to assist in developing an improved production system for organic garlic growers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".