Distribution of Types and Management of Insecticides Based on Technical Applications in Palu Local Shallots
Bibliographic record
Abstract
This study aims to determine the distribution of insecticides and the doses used in local shallots cultivation in Palu valley. This research took place from May to July 2021. This study used a descriptive observational method, describing the condition of farmers in using pesticides on local Palu shallots. Respondents were selected based on their daily activities as local Palu shallot farmers in Palu valley, Palu, Central Sulawesi. Determination of respondents was done using Simple Random Sampling; the total respondents taken were 40 farmers (about 10 people at each site), considering that the sample size could represent the existing population (population condition was homogeneous). Quantitative data were analyzed using a simple cross-tabulation analysis which was then interpreted. The study results showed that Farmer's activity in the local shallot cultivation was inseparable from the use of insecticides in controlling herbivorous insects (95% farmers). Insecticide usage in the Palu Valley area was 67.5% with a frequency of 1 -3 times a week, and 17.5% were applied without heeding the prescribed usage recommendations. There were 16 types of insecticide trademarks circulating in Palu valley, including 3 types in the Maku area, 6 types in Soulove, 4 types in Bolupontu Jaya, and 1 type in Wombo. Insecticide application was mostly in the morning (60-100%), 1 -3 times a week (67.5%), and without rules of use by 17.5%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".