Editorial: Observations and modelling of recent extreme wild fire events and their impact on the environment and climate
Bibliographic record
Abstract
Anthropogenic climate change is known to increase the average global surface mean temperature, intensity 2 of heatwaves, and surface humidity (Pörtner et al., 2022). In a Science Brief Review, Smith et al. (2020) 3 reveal the significant correlation between those factors, i.e. the occurrence of what is called "extreme fire 4weather" (Jain et al., 2022) and the already occurring increase in extent and frequency of wild fires. Through 5 increased fire intensities, "mega fires" can develop, with particularly severe impacts on the atmosphere, 6 environment, and climate. Some examples of mega fires during the past 5 years are:• The British Columbia (Canada) fires from June/July 2017 with around 1,200,000 hectares burned 8• The Siberian fires (Russia) from July 2019 with 3,000,000 hectares burned 9• The Australian fires from September 2019 to January 2020 with 24,300,000 hectares burned 10• The Pantanal rainforest fires (in Brazil) from January to August 2020 with around 380,000 hectares 11 burned and• The California (USA) wildfires from June/July 2021 with around 1,000,000 hectares burned 13 All of the above-mentioned fires are associated with unusually long preceding drought phases alongside 14 very low rainfall quantities and favoring conditions for the outbreak of extreme wildfires such as high winds.15 Those incidences generate unprecedented case studies in terms of their impacts, including environmental
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.018 |
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".