A Review on Mining of Coastal Placer Minerals
Bibliographic record
Abstract
India is bestowed with a long cosat of more of less 7500 km. This long coastal tract has a vast resource of placer minerals such as ilmenite, rutile, leucoxine, monazite, zircon, etc. But this large reserve base does not make India a global player in the market of placer minerals. The production to Reserve Ratio (PPR) in India is 0.001 while it is 0.030 in USA, 0.010 in Australia, and 0.009 in Canada. In order to make India a global player, the production capacity has to be enhanced this requires supportive government policies, positive mindset of authorities, adequate infrastructure etc. This paper critically reviews the methods of exploration of placer Heavy Minerals lying above water-table as well as below water-table. For the minerals available on shore open cut mining is an often used mining method. Off shore placer Deposits can be exploited using water jet pumps and dredging pumps. The paper identifies the key issues and problems related to grant of mining leases, environmental issues etc. that bottleneck the development of placer Mineral Industry in India. For the capacity building of off-shore mining in India, the off-shore mining laws must be framed keeping in view the feasibility of off-shore placer mining as well as environmental implications thereof. The paper emphasizes the need of a pragmatic approach and coordination of all concerned central and state government agencies for the sustainable development of coastal placer minerals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".