Optimization of Cultivated Seaweed Land <i>Gracilaria sp</i> Using <i>Vertikultur</i> System
Bibliographic record
Abstract
The production of Gracilaria sp is still not optimal and there are some opportunities left for production improvement by developing methods that use depth level as a planting media. A vertikultur method using bag nets is more effective and has higher production levels. This research aims to solve problems in cultivating Gracilaria sp seaweed by doing field optimization. Seaweeds were planted as many as 40 points for each 10 levels depth (BRL); 0, 70, 140, 210, 280, 350, 420, 560, and 630 cm from the surface. Environment and seaweed parameters were measured weekly. This research measures daily and weekly growth rate, absolute growth, and the total production. The data were analyzed using Anota test and BNJ further test . The research result shows that physic-chemistry water parameter is suitable for cultivating Gracilaria sp . The lowest weekly growth rate occurred in level BRL 10 in the first week while gaining weight as much as 9.351 gram/week. The highest growth rate occurred in level BRL 4 on the sixth week while gaining weight as much as 32.25 gram/week. The biggest absolute growth occurred in level BRL 4 with 214.87 gram and the lowest occurred in level BRL 10 with 161.45 gram. The biggest absolute growth rate occurred in level BR1 4 while gaining weight as much as 139.87 gram from the original weight and the lowest occurred in level BRL10 with 86.45 gram from the original weight. The best daily growth rate occurred in level BRL 4 with a growth rate of 2.53%/day and the lowest occurred in BRL 10 with 1.84%/day. Total production of Gracilaria sp from level BR1 1 – BR1 10 is 74,840 gram. The biggest production was obtained from level BRL 4 with the production of 8595 gram and the lowest in BRL 10 with 6458 gram. According to the anova test result and beda nyata jujur (BNJ), it shows that depth level (BR1 1 – BR1 10) gave no influence towards the growth rate of Gracilaria sp in the water of Kelagian Island. The vertikultur method by making use of depth level is both feasible and profitable for seaweed cultivators with production level ten times bigger.
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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 | 0.001 | 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".