Dynamics of fires, harvest and carbon stocks in U.S. forests 1926-2017
Bibliographic record
Abstract
Human-fire interactions have always played an important role in the United States of America. Important processes include land clearing with fires in the course of agricultural expansion and development of the West during the 19th century, large-scale fire suppression in the first half of the 20th century and recent “mega-fire” events in California. Strong regional divergences occurred: Fire regimes in the Eastern U.S. were significantly altered due to settlement and land-use changes over the past 100 years, resulting in reduced severity of fire events. In the West the area extent and severity of wildfires has increased, especially in recent decades, arguably due to more frequent climatic extreme events. Although the historical fire narrative in the U.S. has been studied in numerous publications, the links between these developments and changes in the socio-metabolic system i.e., changes in resource use, and consumption, are to our knowledge less well understood. In this study we investigate the influence of anthropogenic alteration of fire regimes on forest biomass Carbon stocks in comparison to forest uses, i.e., the extraction of woody biomass and forest grazing on multiple spatial scales. We develop a long-term reconstruction of biomass burned in forests on the national, regional, and state level based on statistical and remote-sensing data. We describe and examine historical differences between fire regimes in the Eastern and Western United States in connection with human use of forest for the period 1940-2017. Using panel data analysis, we investigate the diverse connection between forest change, socio-metabolic processes, natural disturbances (i.e., wildfires), and associated human fire control on various spatial and temporal scales. With this study we aim to contribute to a better understanding of the underlying socio-metabolic drivers and accompanying processes of altered forest fire regimes.
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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 | 0.002 |
| 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) | 0.002 | 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".