Effect of Various Pre-Treatments and Alternating Temperature on Seed Germination of Artemisia herba-alba Asso
Bibliographic record
Abstract
The present study was designed to determine the effect of alternating temperature regimes (R) and pre-treatments on the achenes germination of Artemisia herba-alba Asso, in order to provide information about germination requirements to be used for the rehabilitation programs. Four alternating temperature regimes with a day-night cycle (16/8 h) were applied R1: (35-20 °C), R2: (30-15 °C), R3: (25-10 °C) and R4: (20-05 °C) and ten pre-treatments were tested for the best regime. This pre-treatments include the pre-soaking in cool water at two durations, pre-soaking in hot water for two durations, pre-soaking in sulphuric acid (0.1 Mol/l) for two durations, pre-soaking in GA3 (Gibberellic acid) for four hours at three concentrations (10-5 M, 10-4 M and 10-3 M) and mechanical scarification with soft sandpaper. Temperature regimes affect significantly at the level 0.05 the final germination percentage and influence the germination rate. The R2 registered the best performances and the increase and the decrease of the temperature reduces the ability of seeds to germinate. Also, the application of the pre-treatments result in highly significant differences (p < 0.01) for the germination characteristics, basically the pre-soaking in cool water during 48h that gave the best overall percentage of germination (71%). The use of GA3 to promote germination was not significant as compared to control even at the highest concentration (10-3 M).
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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".