Extended Fermentation and Physical Scarification to Break Dormancy in Aren (Arenga pinnata) Seeds
Bibliographic record
Abstract
Aren seeds experience physical dormancy due to the thickness, hardness, impermeable seed coat, and the existence of potassium oxalate crystals.In addition to physical dormancy, palm seeds also experience physiological dormancy due to an imbalance in stimulating and inhibiting hormones.The study's objective was to obtain the effectiveness of fermentation and deoperculation methods, both as single and combination treatments, in breaking the dormancy of aren seeds.A factorial completely randomized design (CRD) with two treatments was tested: duration of fermentation and deoperculation treatment.There are four levels of fermentation duration: 0 weeks (F0); two weeks (F1); four weeks (F2); and six weeks (F3); as well as two levels of deoperculation: without deoperculation (D0) and with deoperculation (D1).Data analysis included homogeneity, variance analysis, and the least significant difference test (LSD).All tests were carried out at the 5% level of significance.The result showed that 4-week fermentation treatment combined with deopercolation, and 6-week fermentation treatment with or without deopercolation, were significantly proven to increase the percentage of germination and germination value, accelerate the seeds germination, and not reduce the seed viability of aren.Those three treatments have equal value in all observed parameters of seed germination.
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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".