Historic range of variability of mountain forest ecosystems: concepts and applications
Bibliographic record
Abstract
Concepts of historical range of variability (HRV) have taken on an increasingly important role in resource planning and the management of mountain forest ecosystems. This essay draws on examples from the study of the history of disturbance ecology in the Colorado Rocky Mountains and the southern Andes to examine key HRV concepts and their applications. These case studies show that historical perspectives can reduce the chances of major future surprises in ecosystem conditions related to climatic variation, which often overrides many of the effects of management practices. They demonstrate the long-lasting legacy effects of relatively infrequent but severe disturbances in the past that shaped the present landscape and its potential response to future climatic variation. Finally, the case studies illustrate the importance of conducting area-specific research in potential management areas rather than simply extrapolating research findings from studies of historic range of variability of forest ecosystems conducted elsewhere. Key words: climatic variation, disturbance, Rocky Mountains, Andes, Patagonia, Ponderosa pine, landscape, ecosystem management, fire
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".