STUDY OF WATER QUALITY AND DYNAMIC ANALYSIS OFPHYTOPLANKTONS IN FOUR FRESHWATER LAKES OF MYSORE, INDIA
Bibliographic record
Abstract
Lakes in urban regions are ecological security zones and true indicators of sustainable urban development. They provide opportunities for recreation, study of local aquatic life and ornamental purposes. As a result of increasing land use conflicts and effluent disposal, water bodies and their catchments in the urban regions have been the real casualties. The lakes considered have their specific importance. Lingambudhi Lake was one of the major lakes in Mysore a few decades ago. However, because of development of housing colonies and overgrazing, this lake has lost it’s quality. Hinkal Lake has been a sewage disposal site for the surrounding Hinkal area. Due to lack of natural water, the Government has decided to convert it into commercial areas. Mandakalli Lake provides water for irrigation to the surrounding agricultural areas and is used for fishing purposes. But due to agricultural runoff and sewage inputs, this lake has undergone degradation over the years. Kukkarahalli Lake is a major lake and is used for ornamental and recreational purposes. However, the disposal of treated domestic wastewater has caused severe pollution in the lake. In order for the policy makers and general public to understand the extent of pollution in these lakes, water quality indices have been formulated. In this study, water quality indices which have been used are Canadian Council of Ministers of Environment (CCME) and National Sanitation Foundation (NSF). CCME provides information on quality for all designated purposes of a lake, while NSF provides the quality level, if the lake is being used, or is to be used for drinking purposes. One of the remarkable aspects of the lake environment is the large number of phytoplankton species that are present at any given time. Phytoplankton diversity in four lakes of Mysore has been discussed. Nine diversity indices have been derived using the PASTA soft ware program. An attempt has been made to correlate the presence and interactions of phytoplanktons and the variation of water quality parameters in the considered lakes
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".