Remote Sensing Soils and Social Geographies of Difference: The Landscape Archaeology of Regur from Iron Age through Medieval Period Northern Karnataka, Southern India
Bibliographic record
Abstract
This paper combines analyses of Landsat 8 multispectral data with textual records and diachronic low-density artifact distributions to evaluate how soil differences were incorporated into cultural landscapes around the multicomponent site of Maski, southern India. Spatial analysis indicates that Iron Age (1200–300 b.c.) and Early Historic Period (300 b.c.–a.d. 500) inhabitants differentiated soil types and used more water-retentive, clay-rich soils (regur) for agriculture and sandier soils for locations of metals production. Similar distinctions between soil types are evident in Medieval Period (a.d. 500–1600) inscriptions, but artifact distributions indicate that some inhabitants used less desirable sandier soils for agriculture during the period. Taken together, the distribution, remote sensing, and inscriptional data suggest that social inequalities in access to more valued soils contributed to a socially differentiated landscape by at least the 14th century a.d. and point to the combined role of archaeology and remote sensing to complement and interrogate the historical record.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".