Estimation of Spatio‐Temporal Correlations of Prehistoric Population and Vegetation in North America
Bibliographic record
Abstract
We discuss a simple methodology to enable a statistical comparison of human population with the vegetation of North America over the past 13,000 years. Nonparametric kernel methods are applied for temporal and spatial smoothing of point data obtained from the Neotoma Paleoecology Database and the Canadian Archaeological Radiocarbon Database, which results in sequences of maps showing the development of population and different plant taxa during the Holocene. The estimation of smooth spatial and spatio‐temporal cross‐correlation functions is proposed in order to detect relationships between population and vegetation in fixed time intervals. Furthermore, the effects of varying environment on demographic changes as well as potential impacts of populations on plant taxa over time are analyzed. Pointwise confidence bands for cross‐correlation functions are computed and a robustness analysis is performed to assess the significance of obtained results. Considering the example of oak, an interpretation of our results for eastern North America shows the value of this methodology.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".