Does seed heteromorphism affect the critical temperature thresholds for wild mustard (<i>Sinapis arvensis</i>) germination? A modeling approach
Bibliographic record
Abstract
Wild mustard (Sinapis arvensis L.) is well-known as an important weed for cultivated land, particularly when found among cereal crops. It produces large amounts of heteromorphic (black and brown) seeds. This study aimed to estimate the critical temperature thresholds of wild mustard heteromorphic seeds. For this purpose, a novel Weibull-based thermal time model was developed, which was applied to compare the germination characteristics of the heteromorphic seeds of wild mustard. Germination was investigated by exposing the seeds to eight constant temperatures of 7.5, 10, 15, 20, 25, 30, 35, and 37.5 °C. Over both the sub- and supraoptimal ranges, the proposed model reasonably explained the germination patterns of both seed types in response to temperature. Heteromorphic seeds of wild mustard exhibited different germination behaviors in response to different temperatures. Brown seeds were more cold-tolerant and could germinate rapidly to a high percentage (68%) in a wider range of temperature environments (2.78–38.05 °C); black seeds germinated at a narrower temperature range (4.99–37.97 °C) and a large proportion of black seeds remained dormant (77%). These differences could lead to the temporal distribution of seed germination throughout the growing season.
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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) | 0.000 | 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".