Pre-germination Treatments, Quality of Light and Temperature on Syagrus coronata (Mart.) Becc. Seeds Germination
Bibliographic record
Abstract
Studies show that most species of palm trees present seed dormancy. This characteristic hinders the production of seedlings, due to the long period for germination and the unevenness of the seedlings. The specie Syagrus coronata, despite being widely used as food and economic resources, presents also seed dormancy, which hinders its propagation. Thus, this work aimed at evaluating the germination of S. coronata seeds using different methods of dormancy breaking and also, under different qualities of light and temperature. To do so, the seed endocarps were scarified by friction, puncture and complete removal of the endocarp, and the pre-soaking of seeds at different concentrations (50, 100, 200 and 400 mg L-1) of gibberellic acid (GA3) and indolebutyric acid (IBA). We also evaluated the effect of germination in seeds exposed to different conditions of light (white, red, far red, blue and dark) and to different temperatures (25, 30, 35 and 20-30 °C). The data show that the act of rubbing the endocarp optimizes the seed germination process. Solutions containing growth regulators in the pre-soaking of seeds have a negative impact on germination. And the absence of light and the constant temperature of 25 °C are the most suitable for germination. The results indicate that S. coronata seeds have physical dormancy, and, despite obtaining greater germination in the dark, they are neutral photoblastic.
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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".