Grain size variation in sand column along Chhatrapur coast, Ganjam district, Odisha – A clue to the depositional environment
Bibliographic record
Abstract
The concentration of heavy mineral placer deposits along the coastal tracts are function of various favourable factors i.e. hinterland geological formations, prevalence of favourable climatic condition, their transportation through intricate drainage systems and various coastal processes, which operated during the geological past. Textural analysis of the available unconsolidated sediments from the present deposits is of vital importance to decode the prevailing depositional environment while grain size analysis is the major parameter used. Present study highlights the grain size analysis of the identified sand column from Chhatrapur Mineral Sand Deposit along the coastal tract of Odisha to infer the environment of deposition of heavy mineral bearing sand and their heavy mineral content variation. Standard procedure of sampling, data analysis and interpretation techniques were adopted. Result shows that, sands from frontal and rear dune are characterized by distinct bi-modal distribution, medium to fine, moderately to well sorted with good positive skewness, whereas, sand from Inter-dunal region shows dominant unimodal, medium to coarse grain, moderately poorly sorted nature and slightly positive skewness. The better heavy mineral concentration (10 to 25 % grade) in frontal and rear dunes is attributed to prevalence of aeolian dune deposition accompanied by good sorting. In contrast, the low concentration of heavy mineral (3 to 6 %) in inter dune region is due to occasional fluvial regime and poor sorting of sediments. Thus, grain size analysis can be an effective tool to decipher local prevailing depositional environment, which has a bearing on heavy mineral concentration as well.
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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 | 0.000 | 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".