Holocene fire history of the southern Lake Baikal region
Bibliographic record
Abstract
Holocene fire history of the southern Lake Baikal region The catastrophic fire years that have taken place during the last decade in Siberia and in the boreal forests in general, directly linked to global warming, have had dramatic repercussions on the human populations of these regions. Past fire reconstruction studies are currently the only way to study the past dynamics of these fires and to understand their link with climate, vegetation and human activities. However, few studies of the dynamics of these fires are available in Siberia, and none have been carried out on the scale of the Holocene. This study aims to present the first reconstruction of the fire history during the Holocene based on sedimentary charcoals from two lakes localised on the southern shore of Lake Baikal, in Siberia. Two lakes have been sampled, Lake Ébène and Lake Jarod. The results showed a similar trend between the two lakes,with severe and intense crown fires during the early Holocene and less severe surface fires after 6 500 cal. yr BP. According to pollen reconstructions carried out near the studied lakes, a vegetation transition occurred at the same time. Picea obovata was dominant during the early humid Holocene. After 6 500 cal. yr BP, conditions were drier and Pinus sylvestris and Pinus sibirica became the dominant species. Over the past 1 500 years, the greater presence of human populations has firstly resulted in an increase of the fire frequency, then probably in its maintenance after 600 cal. yr BP in lake Ébène and to finish, in its suppression after 900 cal. yr BP in Lake Jarod. The decrease of fire frequency at the end of the 20th century could be explained by new fire management policies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".