The Boreal Forest of Interior Alaska: Patterns, Scales, and Climate Change
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 iiiTable of Contents Chapter 1 -Introduction to the Boreal Forest 1.1 References Chapter 2 -Fire and Vegetation Patterns 2.1 Abstract 2.2 Introduction 2.3 Methods 2.4 Results 2.4.1 Interior Alaskan Topography 2.4.2Vegetation Distribution in Interior Alaska 2.4.3Fire Distribution in Interior Alaska 2.4.3.1 Fire versus Elevation 2.4.3.2Fire versus Aspect 2.4.3.3Fire versus Slope 2.4.3.4Combined Topography Classes 2.4.4Fire History versus Vegetation Type 2.4.4.1 Flammability of Vegetation Types iv 2.4.4.2 Succession after Fire 2.5 Discussion 2.6 Conclusions 2.7 References Chapter 3 -A hierarchical logistic regression model for vegetation type prediction 3.1 Abstract 3.2 3.6 References v Chapter 4 -Response of the four major vegetation types to changes in climate and fire interval 4.1 Abstract 4.2 Introduction 4.3 Methods 4.4 Results: Response to Climate Change Simulations 4.4.1.Temperature Change 4.4.2Precipitation Change 4.4.3Simultaneous Temperature and Precipitation Change 4.4.4Changes in Fire Interval 4.4.5 Simultaneous Fire Interval Change and Warming 4.4.6 Simultaneous Fire Interval and Precipitation Changes 4.4.7 Areas sensitive to change on the landscape scale 4.4.8Hadley climate prediction for 2100 4.5 Discussion 4.6 References Chapter 5 -Land cover estimates in Interior Alaska across classifications and resolutions vi 5.
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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 | 0.001 | 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".