Bibliographic record
Abstract
Flame weeding is a thermal weed control method that controls weeds through the application of extremely high temperatures. Field experiments were conducted from 2005 to 2007 to determine weed and crop tolerance to flame weeding and to investigate effects on plant development, crop yield, and crop quality. Dose-response curves were constructed for weeds common to horticultural fields in Québec. Flame weeding was more effective in controlling dicot weeds than monocot weeds. Flame doses that reduced common lambsquarters density by 95% (LD95) ranged from 0.83 to 2.85 kg propane km-1 for plants at the cotyledon through the 6-leaf growth stage. LD95 values for redroot pigweed ranged from 1.19 to 2.72 kg propane km-1 for plants at the cotyledon through the 4-leaf growth stage. In shepherd's-purse, LD95 values for weeds at the cotyledon and the 2- to 5-leaf growth stage were 1.15 and 2.78 kg propane km-1, respectively. Control of monocot weeds was poor, with survival greater than 50% for all flame doses evaluated. Onion and broccoli were tolerant of a single flame weeding treatment, with yield losses observed only when flamed within 20 days after transplantation (DAT). Among weed-free treatments, onion was able to withstand up to six flame treatments without any detectable loss in yield. However, flame treatments alone were not able to provide sufficient weed control to maintain yields. Flame weeding had minimal effects on time to reach maturity, leaf and bulb development, pungency or quercetin concentration in onion. Broccoli tolerated up to four flame treatments in weed-free plots without yield reductions. Flame-only treatments had lower yields than the flamed, weed-free treatments in one of two years. Flame treatments had limited effects on the number of days to maturity, leaf development, and glucoraphanin concentration in broccoli. Yield losses in spinach and beets were observed when flamed at both the 4- and 6-leaf growth stages; however, no adverse
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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".