Geological Milieu of the Bijawar Basin based on interpretation of geophysical data; Central India
Bibliographic record
Abstract
SummaryIntegrated interpretation through physical properties of rocks provides better approach towards understanding of geological set-up. High resolution heliborne time domain electromagnetic, magnetic and radiometric dataset acquired over Palaeo Proterozoic Bijawar Basin has been interpreted to enhance the geological understanding. The process involved knowledge driven analysis through correlation with geological units that outcrop and data driven analysis for extrapolation of information. Radiometric data in conjunction with spectral remote sensing data was used for generation of surface geology map. Spatial distribution of rock magnetization properties defines the subsurface extension of litho-structural elements and magnetic basement configuration. Electromagnetic data aided demarcation of resistive basement topography and various conductive layers. Radio-elemental distribution refines the unconformable contact shared by Bijawar Group with overlying and underlying group of rocks and also outlined the formational boundaries of arenaceous-argillaceouscarbonate-volcano-sedimentary sequence. Subsurface extent of basic volcanics is much more than its surface manifestation and appears to extend well below the overlying Vindhyan Supergroup of rocks. The unconformable contact of Bijawar Group with underlying basement and overlying Vindhyan Supergroup is marked by distinct change in conductivity parameter and has been outlined in depth to resistive basement topography. Fault system and its manifestation over different litho-units indicate multiple tectonic episodes.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".