Empirical models of albedo transitions in managed boreal forests: analysis of performance and transportability
Bibliographic record
Abstract
Managing boreal forests for effective climate change mitigation requires comprehension of the full spectrum of climate regulation services that they provide, which includes both the storage of carbon and exchanges of heat and moisture with the atmosphere. It is increasingly recognized that surface albedo is the most important biogeophysical mechanism by which the boreal forest directly influences the global energy balance. Forest management decisions that influence age class and species distributions affect not only the carbon sink capacity, but also the albedo (and hence climate services) of the forested landscape. Disregarding albedo and how it is influenced by management decisions can have profound implications for the effectiveness of any climate change mitigation policy involving active forest management. Here, we explore, analyze, and compare the albedo predicted by simple empirical models with in situ and remotely sensed albedo observations in regions outside the region in which the models were originally developed (southeastern Norway), including boreal Canada and Europe. We find that the models are robust in their ability to predict the longer term interannual trends in the mean winter–summer albedo amplitude, the rapid albedo evolution in young stands, and the timing of seasonal transitions and weak with respect to capturing interannual albedo changes linked to seasonal climate variability and phenology.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".