Survey of farm management and biosecurity practices on shrimp farms on Java Island, Indonesia
Bibliographic record
Abstract
Current information on biosecurity measures implemented by shrimp farmers in Indonesia is limited. This study describes farmer demographic characteristics, on-farm biosecurity practices, farm production and disease status, among small and medium holder shrimp farms on Java Island, Indonesia. A questionnaire-based survey was conducted from November 2019 to May 2020 to collect data from shrimp farms operating in various regions of the Java Island. A numerical score was assigned for each of the assessed biosecurity practices, based on whether it was a conventional risk factor or a protective factor. Based on responses from 90 shrimp farmers, who volunteered to participate in the study, mean overall biosecurity scores ranged from 32 to 54 (out of a maximum score of 100). Most farms (88.9%) either shared common water sources with other aquaculture farms or were connected to other farms via water channel. Farm equipment sharing was common both within (91.1%) and between (41%) farms. Water pre-treatment was common (99%), but approximately a third of the farms did not practice any routine quality assessment for post larvae before stocking. On average, farms produced 1.6 kg/m2 (95% CI: 1.2, 2.0) of shrimp with a harvest size of 43 shrimp/kg (95% CI: 37, 49) or an average weight of 23.3 g at time of harvest. An increasing trend in harvest weight per pond area and shrimp size at harvest was noted with increasing overall biosecurity score. These results indicated that farms with better biosecurity practices tended to have a higher production yield.
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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 | 0.001 |
| 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) | 0.001 | 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".