Application of the hydrotime model to assess seed priming effects on the germination of rapeseed (<i>Brassica napus</i> L.) in response to water stress
Bibliographic record
Abstract
Our aim was to determine whether the parameters of the hydrotime model can be used to compare the effects of seed priming treatments on germination. Seeds of three rapeseed cultivars (‘H61’, ‘H4815’, and ‘DK003’) were exposed to priming treatments and then their germination was tested at different water potentials. Seed priming improved the germination percentage, which was higher in ‘H4815’ than in the other two cultivars, and it increased germination rate (decreased θH) and uniformity (decreased σψb). The increase in germination rate was higher in PEG-osmoprimed than in NaCl-osmoprimed and hydroprimed seeds. The base water potential allowing 50% germination ([Formula: see text]) also was affected by seed priming, but the response of cultivars to different priming treatments varied. Compared with the control seeds, hydropriming of ‘H61’ seeds shifted ψb(50) toward negative values, whereas NaCl-osmopriming of ‘H4815’ seeds shifted ψb(50) toward negative values. On the other hand, priming of ‘DK003’ seeds shifted ψb(50) to more positive values and increased susceptibility to water stress. After dormancy-breaking and dormancy-induction treatments, ψb(50) shifted to more negative or positive values, indicating the release of primary dormancy and induction of dormancy, respectively. Therefore, priming treatments should be selected, depending on the environmental conditions anticipated to prevail during the germination of rapeseed seeds.
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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".