Modeling weed emergence as influenced by environmental conditions in corn in southwestern Quebec
Bibliographic record
Abstract
The timing of weed emergence is of major importance for integrated weed management programs. If accurately forecasted, the timing of weed control operations could be optimized, enabling the implementation of more effective control strategies and consequently, the reduction of herbicide use. Little is known about weed emergence in Quebec. Weed emergence in the field results from the breaking of seed dormancy, seed germination and growth of the seedling to the soil surface. The purpose of this study was initially to review the environmental and cultural factors that affect these mechanisms, to determine which factors are mainly responsible for weed emergence in southwestern Quebec and, finally to develop a weed emergence model. This study focused primarily on common lambsquarters (Chenopodium album L.) and barnyardgrass (Echinochloa crus-galli [L.] Beauv.) since they were the most abundant weeds, representing 37 and 22%, respectively, of the total number of annual weeds in the experimental sites. A 3-year study established that the presence of corn did not affect the density and the pattern of emergence of these weeds. A 2-year experiment demonstrated that rainfall and irrigation had no or little influence on the pattern of weed emergence since soil water content was at or greater than field capacity in the spring. In Quebec, temperature was determined to be the most important factor regulating weed germination and emergence, meaning that weed germination is initiated by a minimal temperature in the spring and that this temperature is specific to each species. Thermo-gradient plate experiments established a base temperature for common lambsquarters, redroot pigweed (Amaranthus retroflexus L.), barnyardgrass, and green foxtail (Setaria viridis L.) of 4, 8, 11, 12 +/- 1 C and <3, >16, 12 and 12 +/- 1 C, respectively for seed lots originating from eastern and southwestern Quebec. These results served as a starting point for the calculation of thermal units for the
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".