Struktur Komunitas Fitoplankton dan Tingkat Pencemaran di Danau Dendam Tak Sudah, Kota Bengkulu
Bibliographic record
Abstract
Dendam Tak Sudah Lake (DDTS) is an aquatic ecosystem and nature reserve located in Dusun Besar Village, Singgaran Pati District, Bengkulu City. Its presence serves not only as a tourist destination and irrigation water supply for rice fields but also as a habitat for phytoplankton. The land use changes around the DDTS Nature Reserve have impacted the water quality of the lake, and phytoplankton are among the biota affected by these pressures. This study aims to assess the phytoplankton community structure and the pollution level in DDTS. The research was conducted by identifying and counting each phytoplankton species found and analyzing water quality. The results of the study showed that the phytoplankton richness in DDTS consisted of 20 species, including 8 species from the Bacillariophyceae class, 6 species from the Chlorophyceae class, 2 species from the Cyanophyceae class, and 4 species from the Euglenophyceae class. The highest phytoplankton abundance was found at the inlet with 1,135.2 individuals/l, followed by the midlet with 1,095.6 individuals/l, and the outlet with 997.2 individuals/l. The diversity index values were high (2.6-2.8), the evenness index was high (0.95-0.96), and dominance was low (0.07-0.08). The saprobic coefficient in DDTS was classified as β-Meso/Oligosaprobic, indicating that the water quality is lightly polluted, with a saprobic index value ranging from 0.6 to 0.87.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".