Strong anthropogenic signals in historic forest fire regime: a detailed spatiotemporal case study from south-central Norway
Bibliographic record
Abstract
To better understand the historic range of variability in the fire regime of Fennoscandian boreal forests we cross-dated 736 fire scars of remnant Scots pine (Pinus sylvestris L.) wood samples in a 3.6 km2 section of the Trillemarka-Rollagsfjell Reserve of south-central Norway. Using a kernel range application in GIS we spatially delineated 57 individual forest fires between 1350 and the present. We found a strong anthropogenic signal in the fire regime from 1600 and onwards: (i) infrequent variably sized fires prior to 1600 shifted to frequent fires gradually decreasing in size during the 1600s and 1700s, with only a few small fires after 1800; (ii) time intervals between fires and the hazard of burning showed substantial differences pre- and post-1600; (iii) fire seasonality changed from late- to early-season fires from the 1626 fire and onwards; and (iv) fire severity decreased gradually over time. Written sources corroborated our results, narrating a history where anthropogenic forest fires and slash-and-burn cultivation expanded with the increasing population from the late 1500s. Concurrently, timber resources increased in value, gradually forcing slash-and-burn cultivators to abandon fires on forest land. Our results strengthen and expand previous Fennoscandian findings on the anthropogenic influence of historic 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".