Laboratory Scale Brachionus plicatilis Culture Technique with Natural Feeds Nannochloropsis oculata and Tetraselmis chuii in Marine Aquaculture Center Lampung, Teluk Pandan, Pesawaran
Bibliographic record
Abstract
A very influential factor in marine fish production is the provision of efficient and appropriate feed and feed management for marine fish larvae. Natural feed has content that can meet the nutritional needs of marine fish larvae. One of the natural feeds that can be utilized in marine fish farming is rotifer (Brachionus plicatilis). This study aims to determine the laboratory scale culture technique of Brachionus plicatilis, determine the growth rate of Brachionus plicatilis with different phytoplankton feeding, determine the optimal phytoplankton density for Brachionus plicatilis. Data collection techniques in this study used 2 kinds of data, namely primary data and secondary data. Primary data comes from observation, interviews, and active participation. While secondary data comes from literature studies, namely books, journals, annual reports, theses, and so on. Laboratory-scale Brachionus plicatilis culture starts from sterilization of tools and materials, planting Brachionus plicatilis seedlings, feeding, and calculating the population of Brachionus plicatilis. Brachionus plicatilis fed with Nannochloropsis oculata at a density of 100,000 cells/ml entered the exponential phase on day 4 with a total number of individuals of 72 ind/ml, while Brachionus plicatilis fed with Tetraselmis chuii at a density of 3000 cells/ml entered the exponential phase on day 5 with a total number of individuals of 100 ind/ml.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".