To waste or not to waste: a multi-proxy analysis of human-waste interaction and rural waste management in Indus Era Gujarat
Bibliographic record
Abstract
Abstract Waste management is paramount to town planning and ancient civilizations across the world have spent resources and mobilized labor for waste disposal and reuse. The study of waste management practices offers a unique window into the daily lives, social organization, and environmental interactions of ancient societies. In the Indus Valley Civilization, known for its urban planning, understanding waste disposal in rural settlements provides crucial insights into the broader socio-economic landscape. While extensive research has documented sophisticated waste management systems in urban Indus centers, little is known about practices in rural settlements. This gap limits our understanding of regional variations and rural-urban dynamics within the civilization. In this paper, using isotopic and microscopic proxies, we characterize the waste disposed of at the rural Indus settlement of Kotada Bhadli to reconstruct the sources of waste, including heated animal dung, and burned vegetation. We propose that rural agro-pastoral settlements in Gujarat during the Indus Era systematically discarded such waste in specific locations. By characterizing waste produced at Kotada Bhadli, we are also able to reconstruct the natural environment and how the natural and cultural landscape around the settlement was exploited by the residents of the settlement for their domestic and occupational needs. Our identification of the attention paid to waste disposal by the inhabitants of Kotada Bhadli adds significant data to our understanding of waste disposal as an insight into past lives.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".