Linking tree rings, summer aridity, and regional fire data: an example from the boreal forests of the Komi Republic, East European Russia
Bibliographic record
Abstract
To evaluate the potential use of tree-ring data as a proxy for fire activity at the scale of a large boreal region, we analyzed a set of regional tree-ring chronologies of Siberian larch (Larix sibirica L.), a spatially implicit annual fire record, and monthly climate data for the Komi Republic for the period 19501990. In most years, annually burned area was below 0.001% of the republic's forested area and reached up to 0.7% during fire-prone years. Principal components (PC) of summer aridity resolved 64.2% of the annual variation in the number of fires, 12.2% in the average fire size, and 59.2% in the annually burned area. In turn, tree-ring PCs explained 65.2% of variation in fire-related weather PCs. Dendrochronological reconstruction of the annual number of fires and of the log-transformed annually burned area predicted 27.0% and 40.1% of the high-frequency variance of these variables, respectively. Coefficient of efficiency, a measure of reconstruction usefulness, reached 0.081 (number of fires) and 0.315 (annual area burned), supporting the obtained index as a realistic proxy for regional fire activity. Decadal variation in coefficient of efficiency values suggested improved monitoring accuracy since 1960 and more effective fire suppression during the last studied decade (19801990).
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".