Beach morphology and coastal protection along Ennore coast in the north of Chennai city, southeast India
Bibliographic record
Abstract
Baseline information on the beach Abstract: morphology along the Ennore coast, an industrial hotspot region in the East coast of Bay of Bengal, is presented. In order to assess the effect of human interference on the coastal zone, a baseline survey of the coastline is considered important. Such information enables to predict the future coastal morphology and settings. Based on the baseline data created, this study predicts that the accretion noticed in the southern side of the Ennore port will affect the port entrance channel in the future course. Therefore it warrants for immediate mitigation measures to curtail such accretion and also recommends for keeping the Ennore creek mouth open permanently. Ennore creek also serves as the coolant source for the two thermal power plant units. The study also put forward certain other mitigation measures which are relevant in the present event of developments. Adjacent to Pulicat Lake, the erosion is continuously taking place at an alarming rate making vulnerable for a striking storm to wash away the villages under the sea. It may also lead to the merger of the Pulicat Lake into the Bay of Bengal, thereby the brackish water ecosystem of the lake may altogether disappear causing several ecological and social problems. The study concludes that a long-term integrated coastal monitoring is the need of the hour
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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 | 0.000 | 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) | 0.002 | 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".