The forest fire regime in Latvia during 1922–2014
Bibliographic record
Abstract
Fire as disturbance of forests has an important ecological and economical role in boreal and hemiboreal forests. The occurrence of forest fires is both climatically and anthropogenically determined and shifts in fire regimes are expected due to climate change. Although fire histories have been well documented in boreal regions, there is still insufficient information about fire occurrence in the Baltic States. In this study, spatio-temporal patterns and climatic drivers of forest fires were assessed by means of spatial and time-series analysis. The efficiency of Canadian Fire Weather (FWI) indices as indicators for fire activity was tested. The study was based on data from the literature, archives, and the Latvian State Forest service database. During the period 1922â2014, the occurrence and area affected by forest fires has decreased although the total area of forest land has nearly doubled, suggesting improvement of the fire suppression system as well as changes in socioeconomic situation. The geographical distribution of forest fires revealed two pronounced clusters near the largest cities of Riga and Daugavpils, suggesting dominance of human causes of ignitions. The occurrence of fires was mainly influenced by drought. FWI appeared to be efficient in predicting the fire occurrence: 23â34% of fires occurred on days with a high or extremely high fire danger class, which overall had a relative occurrence of only 4.3â4.6%. During the 20th century, the peak of fire activity shifted from May to April, probably due to global warming and socioeconomic reasons. The results of this study are relevant for forest hazard mitigation and development of fire activity prediction system in Latvia.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".