Weed Response to Flame Weeding at Different Developmental Stages
Bibliographic record
Abstract
Flame weeding is often used for weed control in organic production and other situations where use of herbicides is prohibited or undesirable. Response to cross-flaming was evaluated on five common weed species: common lambsquarters, redroot pigweed, shepherd's-purse, barnyardgrass, and yellow foxtail. Dose-response curves were generated according to species and growth stage. Dicot species were more effectively controlled than monocot species. Common lambsquarters was susceptible to flame treatment with doses required for 95% control (LD95) ranging from 0.9 to 3.3 kg/km with increasing maturity stage. Comparable levels of control in redroot pigweed required higher doses than common lambsquarters, but adequate control was still achieved. Flaming effectively controlled shepherd's-purse at the cotyledon stage (LD95 = 1.2 kg/km). However, the LD95 for weeds with two to five leaves increased to 2.5 kg/km, likely due to the rosette stage of growth, which allowed treated weeds to avoid thermal injury. Control of barnyardgrass and yellow foxtail was poor, with weed survival > 50% for all maturity stages and flaming doses tested. Flame weeding can be an effective and labor-saving weed control method, the extent of which is partially dependent on the weed flora present. Knowledge of the local weed flora and their susceptibility to flame weeding is vital for the effective use of this method.
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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 | 0.001 |
| 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".