The Analysis of Phytoplankton Abundance Using Weibull Distribution (A Case Study in the Coastal Area of East Yapen in the Regency of Yapen Islands, Papua)
Bibliographic record
Abstract
The coastal waters of East Yapen is one of the spawning sites and areas of care for marine biota in Papua. Because of its very open location, it is widely used by human activities such as fishing, residential, industrial and cruise lines. This indirectly affects the balance of coastal waters condition of East Yapen that impact on the existence of marine biota, especially phytoplankton. Phytoplanktons have a very important role because phytoplankton is the primary producer in the food chain as a link to higher tropical levels. Therefore, special studies are needed such as looking at the distribution of phytoplankton abundance at each site. The data analysis uses the American Public Health Association (APHA), Geo-statistical data, and Chi Square. Then, the distribution parameters are estimated using the Maximum Likelihood Estimation (MLE) method.The obtained parameters are used to describe the cumulative probability and survival of phytoplankton distribution. Samples are taken from fifteen sampling points. The form parameter of the phytoplankton abundance data is 3.9844 and the scale parameter is 79.929. So phytoplankton is the most widely spread in the 15th location, followed by the 6th location. While phytoplankton is at least in the 8th location.The results showthat the highest phytoplankton abundance composition is Bacillariophyceae (50%) and the lowest is Phyrrophyceae (9%) and Cyanophceae. The research is expected to provide an overview of the fertility rate of East Yapen Coastal Waters in particular and Yapen Islands regency in general.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".