Efektifitas Penggunaan Manitoba Trap dalam Surveilans penyakit Bersumber Lalat di Kabupaten Tanah Bumbu, Kalimantan Selatan
Bibliographic record
Abstract
Flies-vector control programs are essential to prevent flies-borne diseases. These activities consist of surveillance to determine the multiplication and distribution patterns of flies, as well as efforts to control flies populations. Flies trap as a surveillance tool was vary according to the interests of the user. Manitoba trap and bottled trap have been widely used to control flies population. This study aimed to determine the effectiveness of both traps type and their modification form for the benefit of the survey, thus it can be a recommendation for flies surveillance program manager. This was a quasi-experimental study design conducted on January 2016 in twocattle farm in Kersik Putih and Segumbang Village, Tanah Bumbu District, South Kalimantan. There were 4 types of trap used in this study: 1) Manitoba trap with black cloth; 2) Manitoba trap with domestic waste or animal excreta as bait; 3) Bottled trap filled with sugar and vinegar solution; and 4) Bottled trap with domestic waste or food scraps. Each type of trap was doubled.Traps was put in pre-randomized spot around cattle cage and leftfor 4 days. The number of flies trapped ranged between 0-55, with Stomoxys calcitrans and Musca Linnaeus as the most species found trapped. Manitoba trap with domestic waste or animal excreta has the highest mean of density compared to the other three trap types. This study showed that Manitoba trap is fairly effective to be used as a surveillance tool in the open area offarm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".