پیش بینی دماهای کاردینال جوانهزنی هویج (Daucus carota L.) و سه گونه علف هرز غالب آن با استفاده از مدل های رگرسیون غیر خطی
Bibliographic record
Abstract
Carrot is a particularly difficult crop to manage in terms of weed control. For weed management of carrot, seed germination is a key process because it determines both the number of weeds that could potentially emerge and the timing of their appearance in the carrot. This study was done to evaluate two nonlinear regression models (Intersected-lines and Dent-like) to describe response of germination rate to temperature in carrot (Daucus carota L.), common chickweed (Stellaria media (L.) Vill.), yellow foxtail (Setaria glauca (L.) P. Beauv.) and canada fleabane (Conyza canadensis (L.) Cronq.). This experiment was based on completely randomized design with 4 replications at Islamic Azad University, Science Research Branch, in 2015. The seeds were treated with different temperatures (2, 5, 10, 15, 20, 25, 30, 35, 40 and 45oC). The analysis of variance showed that temperature had a significant effect on all seed germination percentage and germination rates. Intersected-lines model was superior in carrot and Dent-like model was superior for common chickweed, yellow foxtail and canada fleabane. Base, optimum and maximum temperatures were predicted with appropriate model. Base, optimum and maximum temperatures were for carrot 1.67, 22.84, 43.16; common chickweed 3.58, 18.82-19.67, 42.75; yellow foxtail 14.17, 33.75-34.92, 44.86 and canada fleabane 13.74, 31.73-31.94, 44.21ºC, respectively. This results showed that carrot germinated earliest among the studied species, because it had the lowest base temperature, so sooner planting it was caused sooner carrot establishment and less weed competition.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".