Can alternating temperature, moist chilling, and gibberellin interchangeably promote the completion of germination in <i>Clematis vitalba</i> seeds?
Bibliographic record
Abstract
Each plant species has particular requirements for seed germination, and some of them respond differently to constant or alternating temperature regimes. In this study, the interchangeable effects of different treatments and temperatures on the completion of seed germination of Clematis vitalba L. were investigated. The seeds were tested with a constant (from 5 °C to 25 °C) or a fluctuating (25/10 °C) temperature regime, and the effect of gibberellic acid (GA3), incubation at warm (W) or cold (C) temperatures while being imbibed, and drying after ripening (DAR) were evaluated. The final germination percentages and the time in days required to reach 50% of germination (T50) were calculated. GA3 and C significantly enhanced completion of seed germination at all of the temperatures tested. A strong positive effect of alternating temperature was observed, which triggered completion of seed germination regardless of treatment. Under the fluctuating temperature, the chilled seeds had the most rapid germination. Low germination rates were observed for both control and DAR treatments. Seeds of C. vitalba display a certain degree of dormancy, which can be broken by moist chilling and GA3 treatments. Moreover, alternating temperature stimulates the completion of seed germination by satisfying certain physiological requirements for germination under constant temperatures.
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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 | 0.001 |
| 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".