GIS Based Approach to Determine the Changes of Water Hyacinth (Eichhornia crassipes) Cover and Relation with Lesser Whistling Teal (Dendrocygna javanica) Assemblage at Santragachi Wetland, West Bengal
Bibliographic record
Abstract
The present investigation is conducted to study the year wise (2011 to 2018) changes of water hyacinth (Eichhornia crassipes) cover at Santragachi Lake a Wetland under National Wetland Conservation Programme of India. Further the relationship between water hyacinth cover and the most abundant migratory waterbirds of Satragachi, Lesser Whistling Teal (LWT; Dendrocygna javanica) is assessed because this bird species is fully depended on water hyacinth mat for their roosting. The study comprises of eight satellite images procured from Google earth (2011 to 2018) to explore this relationship. A marked decline in the number of LWT at Santragachi wetland is observed in the year of 2017 and 2018. It is very interesting fact that from 2017-2018, the water hyacinth mat of this wetland is almost cleared before winter and the result of cluster analysis supports this fact. Significant positive correlation is also observed within LWT number and water hyacinth cover area (r = 0.7481 at p< 0.05) along with the total perimeter (r = 0.8648 at p< 0.05) of the water hyacinth islands at Santragachi wetland. However, open water area is also needed for diving, swimming, food searching for the LWT and other waterbirds. Therefore, more study is needed to optimize the clearing operations, focused on optimizing the shape and size of water hyacinth islands for proper management of the waterbirds habitat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".