Evaluation of biodegradable mulches for production of warm-season vegetable crops
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.004 | 0.001 |
| 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 | 0.001 |
| 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 it