The Species Composition of Phytoplankton and Nutrient Content in the Nipa Nypa fruticans Ecosystem on the West Coast of Aceh, Indonesia
Bibliographic record
Abstract
Nipa Nypa fruticans grows in estuaries that are affected by sea waters and freshwater. Therefore, the Nipa habitat develops into an area of water mass circulation and becomes rich in nutrients that trigger the growth of phytoplankton as the primary producer in the aquatic ecosystem. This study aimed to examine the species composition and distribution of phytoplankton and nutrient content in the nipa ecosystem on the west coast of Aceh, Indonesia. It was conducted from July 2018 to September 2019 and sampling was carried out in two areas, namely Kuala Bubon Aceh Barat district and Kuala Tadu, Nagan Raya district. A total of 100L of surface water samples was taken from three points at each location. Subsequently, the water was filtered with a plankton net mesh No. 25 and preserved with a 4% Lugol solution. The water sample from both locations was also analyzed for nitrogen and phosphorus contents. The results showed that there were 20 species of phytoplankton in both sampling locations, with Kuala Bubon consisting of 13 species with density of 160.87 cells L-1, and 15 species in Kuala Tadu with density of 273.64 cells L-1. Haramonas sp. was the dominant species in Kuala Bubon, while Amphisolenia bidentata was dominant in Kuala Tadu. Based on the nutrient content (nitrate+nitrite and phosphate), the average nutrient content in Kuala Bubon was 1.31 mg L-1 (mesotrophic) and 0.12 mg L-1 phosphate (hypertrophic). Meanwhile, Kuala Tadu had 1.078 mg L-1 nitrate+nitrite (mesotrophic) and phosphate, 0.22 mg L-1 (hypertrophic) waters. These findings are an early indication that the estuary of Kuala Bubon and Kuala Tadu have been contaminated by organic matter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".