Effect of two different thermal units and three types of mulch on weeds in apple orchards
Bibliographic record
Abstract
The effect of two different thermal units (flame and hot steam) and three types of mulch on the percentage of weeds killed was studied in a series of experiments over 2 years. The factors studied were driving speed (2, 3, 4 km/h), flame treatment (first, second, third), growth stage (<6, 68, >8 true leaves), hot steam treatment (single, double), mulch type (none, coarse bark, sawdust, hay), and chemical application. The results suggest that a driving speed of 2 km/h kills the highest percentage of weeds, and for weed species with unprotected growth points and thin leaves, the first flame application can completely kill weeds with <6 leaves. However, a second or third flame application is required for those with 6 or more leaves. The hot steam method is effective when it is applied twice, with the second application 1 week after the first. However, there is room for improving its technology to make it cost effective for large-scale applications. Mulches after chemical herbicide application are effective for controlling weeds. However, mulching cannot be recommended with flaming because of fire hazard. The effectiveness of herbicide depends on the weed species and on whether the same herbicide was used in the preceding years. Compared to using herbicide with mulching, herbicide alone was less effective in controlling weeds and more costly in terms of cost per hectare and the environment. Key words: thermal weed control, flame, hot steam, mulching, herbicides, apple orchard, logit models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".