Fresh market sweet corn production with clear and wavelength selective soil mulch films
Bibliographic record
Abstract
Earliness of fresh market sweet corn (Zea mays L.) is important to increasing profitability and maintaining market occupancy. Maturity of fresh market sweet corn may be advanced by the use of plastic soil mulch films. In 2000 and 2001, the effects of clear (CMF) and wavelength selective (WLSMF) mulch films on soil temperature and moisture and the performance of fresh market sweet corn with and without N fertilization were evaluated in a Granby loamy sand soil in southwest Ontario. Both mulch films increased soil temperature and moisture compared with bare soil. Soil temperatures were 1.8°C higher at 5 cm and 1.6°C higher at 15 cm soil depth under CMF than WLSMF averaged over the growing season in two years. Both mulches increased soil moisture levels relative to bare soil, but less increase occurred under CMF than WLSMF. Both CMF and WLSMF advanced sweet corn maturity by 6-7 d relative to the bare soil. Compared with bare soil, marketable yields increased by 25 to 63% without added N and by 72 to 114% with added N under CMF. Under WLSMF, the corresponding increases in marketable yields were 97 to 98% without added N and 120 to 200% with added N. While WLSMF was superior to CMF for increasing fresh market sweet corn yields in southwestern Ontario, the relative economic advantage of each mulch type needs to be studied. Key words: Marketable yield, nitrogen, soil cover, soil moisture, soil temperature
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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 | 0.000 | 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) | 0.001 | 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".