Effects of stocking density on the growth rate of gold fish fry reared in hapa
Bibliographic record
Abstract
The present study was conducted to investigate effects of stocking density on growth performances of gold fish (Carassius auratus) in hapas. Experiment was conducted for a month with three treatments where three stocking densities were T1 (10 fry/hapa), T2 (15 fry/hapa) andT3 (20 fry/hapa) each having three replications which were selected randomly. In the present experiment hapa (3ft × 2ft × 2ft) with 1 mm mesh net was used. Gold fish fry having a mean body weight of 0.007 g were used in all treatments. Fishes were fed at the rate of 10% of their body weight containing 34.11% protein. Water quality parameters were monitored at 10 days interval and the ranges were –temperature 24.75 to 27.75 oC, dissolved oxygen 3.68 to 4.09 mg/L, pH 7.3 to 8.16, ammonia 0.3 to 1 mg/L, nitrite 0.01 to 0.03 mg/L, phosphate 0.6 to 1 mg/L and alkalinity 119 to 187 mg/L. At the growth performances were evaluated by comparing mean final body weight, specific growth rate and food conversion ratio. The present study showed that the gold fish fry in T1 resulted the best mean final weight gain (1.188 g) followed by T2 (0.834 g) and T3 (0.686 g). The SGR ranged between 6.64 and 7.43% per day and FCR ranged between 3.56 and 4.12 with T1 showing the lowest FCR. The survival rate (%) ranged between 76.67% to 85.67%. From the present experiment it was found that individual fish growth rate was decreased with the increase of stocking density.Asian J. Med. Biol. Res. December 2017, 3(4): 504-515
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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 | 0.000 | 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".