Constraints on the deep thermal structure of the Dharwar craton, India, from heat flow, shear wave velocities, and mantle xenoliths
Bibliographic record
Abstract
[1] We have used constraints from seismic shear wave vertical velocity profiles, geothermobarometry estimates on mantle xenoliths, and surface heat flux and heat production measurements to analyze the thermal regime of the deep lithosphere beneath India. In the Dharwar craton of southern India, the shear wave velocity gradient in the mantle, as well as xenolith geothermobarometry data, suggests a low mantle heat flux, 14–20 mW m−2, consistent with surface heat flux measurements. However, for standard cratonic mantle composition, seismic velocities require Moho and mantle temperatures to be about 300 K higher than inferred from heat flux and xenolith data. This discrepancy can be only resolved by changing the mantle composition, specifically by increasing the Fe number. The shear wave velocities are highest beneath north central India, where calculated S wave travel times are 2 s shorter than in the Dharwar craton. These differences in traveltime and the very steep gradient in the shear wave velocity profiles in north central India cannot be explained by variations in mantle temperature but require differences in mantle composition.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".