The Boreal Forest of Interior Alaska: Patterns, Scales, and Climate Change
Why this work is in the frame
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Bibliographic record
Abstract
According to a variety of field observations, most forest types of the boreal forest in Interior Alaska can be found at unique elevation ranges and topographic slopes and aspects. My analysis of spatial interactions among fire, vegetation type, and topography at 1km resolution suggests that these spatial patterns are still represented at this scale. In order to understand drivers of vegetation type distribution and change, a hierarchical logistic regression model was developed. The model indicates that the distinction between tundra versus forest is driven by elevation, precipitation, and south to north aspect. The separation between deciduous forest versus spruce forest is driven by fire interval and elevation. The identification of black versus white spruce uses fire interval and elevation as the main drivers. The model was validated in Interior Alaska and Northwest Canada where it could predict vegetation with good accuracy. The logistic regression model could also be used to distinguish bog vegetation from all other vegetation types and improved in predictive ability when actual fire history was included in model development. The model was then used to identify vegetation response to environmental change by imposing changes in temperature, precipitation, and fire interval. Black spruce remains the dominant vegetation type under all scenarios expanding most under warming coupled with increasing fire interval. White spruce is clearly limited by moisture once average growing season temperatures exceed 2°C. Deciduous forests expand their range the most when decreasing fire interval, warming, and increasing precipitation are combined. Tundra is replaced by forest under warming but expands under precipitation ii increase. Model predictions agree with current knowledge of the response of vegetation types to climate change. The response of vegetation types to environmental changes is not linear when two changes are imposed simultaneously. The last chapter explores the compatibility and accuracy of currently existing classifications for Interior Alaska and the effect of scale. Overall agreement among the classifications is very low; low kappa values indicate that much of the agreement among the classifications can be attributed to random chance. The resolution of the vegetation classifications affects the representation of vegetation types: the major vegetation types eliminate the less abundant types with increasing coarseness. Note: Abstract extracted from PDF text
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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 it