Beyond pre-Columbian burning: the impact of firewood collection on forest fuel loads
Bibliographic record
Abstract
Government agencies in the United States (US) adopted a prescribed burning policy based in part on paleo-environmental evidence of pre-Columbian Native American burning regimes. However, biomass collection by Native Americans in the pre-Columbian era left little direct or indirect evidence of its magnitude or influence on fire regimes. In many developing countries, local peoples harvest biomass for shelter, tool production, cooking, and heating, and often manage forests communally. The objective of this study was to use modern proxy biomass collection estimates analogous to pre-Columbian era practices in the western US to estimate the potential impacts of regionwide firewood collection on fuel loads in the Sierra Nevada range of California. A minimum of 59% of the forested area of the Sierra Nevada range could have been completely stripped of surface fuel accumulation in the 100-hour (100 h) fuel moisture class (2.54–7.62 cm diameter) each year in the pre-Columbian era, but upper estimates suggest Native American fuelwood requirements may have exceeded the amount of 100-h surface fuels accumulated over the entire range each year. The collection and removal of the fuels from the surface fuel loads may have contributed to reduced fire severities over that era. Dead wood collection in Nepal and India was found to reduce the threat of forest fires. Including the effects of cultural practices on fuel loads may improve reconstructions of past fuel and fire regimes and may benefit modern management strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".