Investigating Aquaculture Management Practices and Challenges in Selective Aquaculture Hatcheries Across Khyber Pakhtunkhwa, Pakistan
Bibliographic record
Abstract
Aquaculture is a rapidly expanding food production industry worldwide, involving the controlled or semi-controlled rearing of aquatic animals. This study aimed to gather data on the management practices and challenges in aquaculture hatcheries across Khyber Pakhtunkhwa, Pakistan. The data were collected through structured surveys, field visits, interviews, and focus group discussions with aquaculture hatchery managers, workers, and relevant government officials, focusing on the management practices of fish hatcheries. Warm-water hatcheries had a larger proportion than cold-water hatcheries in terms of surface area, and the numbers of ponds varied between 10 and 80, with a ratio of 60% technical to 80% non-technical staff. The most commonly cultured fish in warm-water hatcheries include Cirrhinus mrigala, Hypophthalmychthys molitrix, Ctenopharyngodon idella, Labeo rohita, Catla catla, and Carassius auratus. Cold-water hatcheries contained Salmo trutta, Oncorhynchus mykiss, and Oncorhynchus mykiss kamloops, while semi-cold water hatcheries had Tor putitora and Carassius auratus. Only 46.70% of hatcheries use hormonal applications for breeding success, such as ovaprim, ovatide, and MS-222. Brooders in hatcheries were fed with oryza, AMG, aquafeed, wheat bran, rice bran, soybean oil, and grasses. The feed of fries consisted of a combination of oryza, AMG, aquafeed, supreme feed, rice bran, wheat bran, egg yolk, soybean, and Chenab feed. The common diseases found in hatcheries across Khyber Pakhtunkhwa were fin rot, proliferative kidney disease (PKD), saprolegnia, branchiomycosis, lernaesis, argulosis, fish ulcer, dropsy, and whirling diseases. The study concluded that most of the hatcheries in Khyber Pakhtunkhwa faced problems, such as the unavailability of laboratories, incomplete staff, electricity problems, water scarcity, infected stream water, and waterlogging. The government is recommended to overcome these basic problems and improve hatchery production to stimulate the province’s economy. TRANSLATE with x English Arabic Hebrew Polish Bulgarian Hindi Portuguese Catalan Hmong Daw Romanian Chinese Simplified Hungarian Russian Chinese Traditional Indonesian Slovak Czech Italian Slovenian Danish Japanese Spanish Dutch Klingon Swedish English Korean Thai Estonian Latvian Turkish Finnish Lithuanian Ukrainian French Malay Urdu German Maltese Vietnamese Greek Norwegian Welsh Haitian Creole Persian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back //
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".