Bibliographic record
Abstract
Paleoecological data provide insight into patterns of change in vegetation and in the factors, such as climate and disturbance, that cause vegetation change. Disturbance by fire, insect, and mammalian herbivores and, in floodplains, flooding are the primary drivers of changes in population structure, community composition, and species distribution in the boreal forest on time scales of years to decades (Chapter 7). On longer time scales, such as centuries to millennia, the role of variation in regional climate in determining compositional changes in the boreal forest is also clearly visible. Variability in regional climate may act directly on boreal species (e.g., causing changes in species distributions) or indirectly, by altering disturbance regimes. Proxy records of environmental and ecological change (e.g., pollen and macrofossils in lake sediments, tree rings) are selective in the kind of information they record. Evidence of fires, for example, is more persistent and thus better represented in the paleoecological record than is evidence of mammalian herbivory. For this reason, our understanding of long-term patterns of compositional and structural change in the boreal forest is limited to an analysis of the effects of a few key drivers of change, primarily climate and fire. In this chapter, we offer a long-term perspective on changes in climate and disturbance regimes and their relationship to major changes in vegetation. We first consider multimillennial time scales and discuss the role of climate and disturbance in driving the two major vegetation transitions that have occurred during the Holocene (the past 12,000 years). We then explore evidence for spatial and temporal variation in disturbance regimes during the late Holocene. Much of the terrain that is currently occupied by the Alaskan boreal forest remained ice-free during the glacial episodes of the Quaternary period (Pleistocene and Holocene), which spans the past 1.8 million years. Alaska forms part of the largely unglaciated Beringian region (named after the Bering Strait that lies at its heart; see Hopkins 1967) that extends from the Kolyma River in Siberia to the MacKenzie in northwest Canada and constitutes ca. 30% of the circumboreal zone.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".