Analysing the Physical, Demographic and Vulnerability Profile of Indian Coastal Zone
Bibliographic record
Abstract
Indian coastal zone, comprised of the districts of various states, has widely varying physical and demographic characteristics. It also has a varying risk profile with respect to the cyclonic storms that cross the coastline during monsoon period every year. Vulnerability of the coastal zone depends upon both the risk arising from them and the exposure area characteristics i.e., physical and socio-economic characteristics of the coastal areas. In this paper, an attempt is made to depict the vulnerability profile of Indian coastal zone in terms of exposure area characteristics and the storm risk profile. Here, the physical characteristics of the coastal zone are captured in the form of coastal insularity, the profile of which is relatively less known. Further, population density and the concentration of population in coastal cities represent its socio-economic characteristics. An attempt has also been made to establish statistical relationship between coastal insularity and other variables like population density and agriculture production and it has been found that both of them increase with increasing insularity i.e., greater the stretch of coastline, more it attracts population; while also resulting in more economic activities, thereby increasing their vulnerability. However, the coarse vulnerability of the coast, crudely defined as a product of population density and coastal insularity, has shown an irregular variation across the coast, with the South-Western part being more vulnerable than other parts of the Indian coast.
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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 | 0.001 | 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) | 0.001 | 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".