Effect Of Canola Meal And Corn Gluten Based Diets On Growth, Body Composition, Blood Chemistry And Fatty Acid Profile Of Rohu (Labeo Rohita) At Selected Farm Sites In Punjab Province, Pakistan.
Bibliographic record
Abstract
The main hurdle in steady development of the aquaculture sector is the unavailability of balanced and cost-effective feed which accounts for almost 60% of total farming cost. To resolve this issue, current study was conducted in semi-intensive earthen ponds in district Ali Pur Chattha and Mundi Bahaudin of province Punjab (renowned aquaculture hub in Pakistan) to evaluate the effect of canola and corn gluten-based diets on the growth, body composition, hematology and fatty acid composition of Labeo rohita. 1000 fishes stocked per acre having average weight of 198 ± 19 g at the start of trial and divided in to three groups on the stance of iso-nitrogenous and iso-caloric diets having crude protein of 30% viz. canola-based diet, corn gluten-based diet, and control diet made by 50:50 mixing of canola and corn gluten diets. Analysis of variance (ANOVA) was applied on collected data and results revealed that fish fed with canola-based diet had significantly more increase in body weight as compared to corn gluten and control diets. Significantly higher values of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) as well as higher blood PROT and GLOB were recorded in fish of control diet. These findings indicate that Labeo rohita showed better growth with canola-based diet due to greater availability of protein from this source and healthy blood and fatty acid composition with control diet, respectively.
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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 | 0.001 | 0.001 |
| 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".