A multi-proxy reconstruction of anthropogenic land use in southwest Asia at 6 kya: Combining archaeological, ethnographic and environmental datasets
Bibliographic record
Abstract
Land use and land cover (LULC) changes have important biophysical and biogeochemical effects on climate via a variety of mechanisms. Several climate modelling studies have demonstrated the impact of LULC scenarios on past climate reconstructions. Testing the impact of anthropogenic land use on mid-Holocene climate thus requires reconstructions of land use that accurately reflect this time frame. To address these concerns, the PAGES LandCover6k working group aims to create data-driven gridded global reconstructions of land use and land cover to provide the climate modelling community with inputs for sensitivity testing of the impact of LULC changes on global climate. As one of the earliest global centres of domestication, agricultural production, and population nucleation, Southwest Asia represents one of the areas of the world expected to display the greatest land use impact and human-induced land cover change at 6 kya, and is therefore critical for the mid-Holocene time frame. Here, we reconstruct land use for Southwest Asia for the 6 kya time frame at a regional scale. We draw on environmental data to reconstruct the range of possible land uses within each particular environment and on archaeological and historical data to reconstruct actualized land use. We then compare this reconstruction to common global LULC models, including the most recent HYDE and KK10 iterations. The reconstruction presented here differs from these previous reconstructions in its methodological approach, spatial extent and resolution. It also differs from both models in population density distribution and land use allocation. While the output of our reconstruction is generally more similar to HYDE 3.2 than KK10, particularly in terms of reconstructed pastoral land use, we model greater agricultural land use than HYDE across the entire region, and less land use overall compared with KK10. The paper provides a method for systematically incorporating archaeological data into models of past land use and demonstrates the value of such an approach for enhancing empirical validity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".