Extract of Nerium oleander L. Effectively Inhibit Population of Spodoptera exigua (Hubner.) on Palu Shallot
Bibliographic record
Abstract
One of the obstacles in shallot cultivation is the S. exigua Hubner larvae attack, reducing crop yields. The efforts to control larvae attack using chemical pesticides are often carried out. One of the alternatives chosen to control the larvae attack is the use of Nerium (Nerium oleander L.) leaf extract. This plant has the potential as a larvicide because it is toxic. The study's main goal was to figure out what effect a certain concentration of N. oleander leaf extract had on the population density and attack intensity of S. exigua larvae. The investigation was carried out between December 2018 and February 2019. P0= 0 g/l (without treatment), P1= 2.68 g/l (0.268%), P2= 5.37 g/l (0.537%), P3= 10.75 g/l (1.075%), P4= 21.5 g/l (2.15%), and P5= 43 g/l (4.3%) were employed in the study. The randomized block design (RBD) was utilized in the study, and it was repeated four times. The findings revealed that increasing the quantity of N. oleander leaf extract may reduce the population density and attack intensity of S. exigua larvae while simultaneously increasing the output of Lembah Palu shallots. Generally speaking, the higher the concentration of N. oleander leaf extract, the lower the population density of S. exigua larvae, and the larger the shallot yield. It is necessary to use the effective concentration of N. oleander leaf extract, which is P3 (10.75 g/ha) with a production rate of 7.29 tons/ha in order to get the desired results.
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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".