Reconstructing a Prairie-Woodland Mosaic on the Northern Great Plains: Risk, Resilience, and Resource Management
Bibliographic record
Abstract
Recent geological evidence of high-amplitude, short-term, climatic variability on the northern Plains in the late Holocene implies that significant fluctuations in resource availability may have regularly occurred on the scale of human generations. In this region, evidence of high mobility, low population density, and storage are generalized responses of hunter-gatherer populations to the effects of environmental variability on resourcepredictability. In order to achieve more sophisticated understanding of the relationship between risk, environment, and land-use for the last few thousand years, we suggest that multidisciplinary reconstruction of detailed landscape histories is necessary. This is so because landscape histories may encode: (1) spatial and temporal variability in habitat diversity (i.e., patchiness); (2) geographical differences in ecosystem resilience and resistance to short-term macro climatic variability; and (3) enhancement of resource predictability or diversity through lnanagement practices such as anthropogenic burning. Modern vegetation surveys, presettlement landcover reconstructions, and recent geomorphic and paleo vegetation data from the Oak Lake Sandhills, Manitoba, Canada, are assembled to illustrate these points.
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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 | 0.001 |
| 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".