Soil Outperforms Rice Husk Ash as a Seedling Medium for LUSI Glutinous Rice in Suboptimal Swampy Lands of South Sulawesi, Indonesia
Bibliographic record
Abstract
The utilization of vast suboptimal land in Indonesia is important to support food security.Good seedling management is a key strategy in the sustainable utilization of suboptimal land, using soil media (coastal swamps of Lake Tempe (TLS), coastal swamps of the Walanae River (WRS), Telotenreng pumped land (TPL)) and rice husk ash.The objectives of the experiment were to assess the time of shoot formation (days), leaf formation (days), 50% shoot formation (days), seedling height at 8, 15, and 22 days (cm), and the number of plants per tray (plants) in LUSI glutinous rice.The experimental method used RBD with three replications on five types of seedling media: soil media, husk ash media, soil + husk ash 1:1 media, soil + husk ash 2:1 media, and soil + husk ash 1:2 media.The experimental results showed that shoot formation (TLS fast 1.17 days, TLS slow 5.00 days), complete leaf formation (TLS fast 3.00 days, TLS slow 7.33 days), 50% shoots (TLS fast 2.17 days, TLS slow 6.00 days), plant height (WRS tall 33.36 cm, TPL short 8.50 cm), and number of plants per tray (TLS tall 1,385.36seedlings, WRS short 101.33 seedlings).The conclusion is that the soil medium is the best treatment for all parameters in the three soil medium locations.Therefore, for rice farmers in the suboptimal swampy areas of South Sulawesi, we recommend using local soil as an economically effective seedling medium for LUSI glutinous rice.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".