Temporal Variations of Micro Benthic Assemblage in the Sangu River Estuary, Bangladesh
Bibliographic record
Abstract
Temporal distribution of shellfish assemblages, together with water quality data, was conducted in the Sangu river estuary of Bangladesh to assess shellfish's diversity index during winter, pre-monsoon, and monsoon and post-monsoon periods. A total of 15 species of shellfish belonging to 9 families was recorded of which Acetes sp. (25.18%), Matuta victor (18.77%), Exopalaemon styliferus (18.28%), Parapenaeopsis sculptilis (14.22%) were found to be most dominant species during the study period. Significant temporal differences were observed for water temperature, salinity, water transparency, P H and DO. The diversity indices, Shannon-Wiener diversity index and Margalef richness index showed a significant difference among the seasons while no significant difference was observed in the Pielou’s evenness index and Simpson dominance index. The analysis of similarity (ANOSIM) was used to test for significant differences in species assemblages between sampling seasons. At the similarity of 74.8%, three groups were attained while winter and pre-monsoon showed separate clustering from other groups. The Non-metric Multidimensional Scaling (nMDS) showed 50% similarity in all seasons based on Bray-Curtis similarity matrix. The CCA ordination indicated that temperature was the most important environmental parameter shaping the shellfish assemblage structure.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".