Evaluation of biodegradable mulches for production of warm-season vegetable crops
Bibliographic record
Abstract
While plastic soil mulches are widely used in vegetable crop production, removal and disposal of these plastics at the end of the growing season is costly and environmentally unsound. This trial assessed the field performance of several colors of corn-starch-based biodegradable mulches for the production of warm season vegetable crops (sweet corn, zucchini, cantaloupe, pepper and eggplant) over three cropping seasons in Saskatchewan. The clear and wavelength selective types of mulch most commonly produced beneficial effects on the rate of crop development and yields. This may be attributed to these mulch types enhancing soil temperatures, especially early in the growing season. There were no appreciable differences in the soil temperatures or crop growth and yield responses on the biodegradable mulches as compared with the same color of standard low-density polyethylene mulch. The biodegradable mulches were easy to apply and were readily incorporated into the soil at the end of the growing season. Although the clear and to a lesser extent the wavelength selective forms of biodegradable mulch tended to break down well before the end of the growing season, this early failure did not negatively impact the performance of any of the crops tested, as long as supplemental weed control was provided. Supplemental weed control would be more important for slow-growing, erect crops like peppers and eggplants than for the more robust and sprawling crops like corn and melons. Although the biodegradable mulches are more expensive than the corresponding standard polyethylene-based plastics, this added cost is more than offset by the costs to remove and dispose of the standard plastic mulches. Key words: Sweet corn, pepper, zucchini, eggplant, cantaloupe, Biotelo
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".