Per Capita Land Use through Time and Space: A New Database for (Pre)Historic Land-Use Reconstructions
Bibliographic record
Abstract
Anthropogenic land cover change (ALCC) models, commonly used for climate modeling, tend to utilize relatively simplistic models of human interaction with the environment. They have historically relied on unsophisticated assumptions about the temporal and spatial variability of the area needed to support one person: per capita land use (PCLU). To help refine ALCC models, we used a range of data sources to build a new database that attempts to bring together PCLU data with significant time depth and a global perspective. This new database can provide new nuance for our understanding of the variability in land use among and between time periods and regions, data that will have wide applicability for continued research into past human land use and present land-use change, and can hopefully help improve existing ALCC models. An example is provided, showing the potential impact of new PCLU data on land-use mapping in the Middle East at 6000 BP.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".