Selecting a suitable diversity index for a tropical Ramsar wetland site
Bibliographic record
Abstract
Abstract Wetlands are under threat from the inflow of urban pollutants on a daily basis. The phytoplankton community is the most affected by increasing eutrophication. Biodiversity based on species richness and evenness can reflect the phytoplankton community composition, as well as describing the water pollution impacts on biotic communities. Eutrophication is a major problem in tropical wetland systems because they receive various waste discharges. Therefore, an attempt was made in this study to analyse the eutrophication status of a tropical wetland (part of the Vembanad Wetland in Cherthala‐Aroor‐Edakochi coastal belt, Kerala) that is being affected by seafood effluent discharges. Alpha indices (Shannon–Weiner Diversity Index; Simpson Index) and beta diversity (Jaccard Index and Sorensen Index) were used to identify appropriate diversity index in a eutrophic environment. Analysis of the plankton populations indicated significant variations among the wetland sampling sites, with the highest percentage of pollution indicators observed in the interconnected channels than in the main portion of the wetland. The results of this study also reveal that a dangerous level of reduction in Chlorophyceae occurred which, in turn, affects the wetland primary productivity. If this situation continues, the wetland will become dominated by fewer, more pollution‐tolerant species over time, indicating serious attention must be given to stopping the loss of diversity in the wetland. This study revealed that β diversity indices are more suitable for studying diversity in a eutrophic wetland system than α diversity indices, mainly because β diversity indices indirectly consider the pollution indicator species, whereas the Shannon Index fails to do so. This study also notes the importance of using the Simpson Index over the Shannon Index for eutrophic waterbodies.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".