Consumption of the organic layer in southern Sweden during fire events and correlations with the Canadian Forest Fire Weather Index (FWI) risk ratings
Bibliographic record
Abstract
The occurrence and thickness of organic layers in forests can significantly influence fire behavior. Complete understanding of the consumption of these layers \nduring fire events is a knowledge gap that exists in southern Sweden. In this region, sixteen burned sites were measured for fermentation and humus layer thickness. \nThese measurements were compared to those collected on an adjacent non-burned control site. Consumption was calculated to correlate with the numerical rating \ncomponents of Duff Moisture Code (DMC), Drought Code (DC), Build-Up Index (BUI), and Forest Fire Weather Index (FWI) of the overall Canadian Forest Fire Danger Rating System (CFFDRS). \n \nThe FWI numerical ratings focused upon in this study were found to be appropriate indicators for relative amounts of fermentation and humus organic layer consumption attributed to fire events in southern Sweden. In particular, variation of fermentation layer consumption was most clearly associated with the DMC, humus layer consumption with the DC, and total (fermentation and humus) organic layer \nconsumption with both the DC and the BUI. \n \nOn sites where root exposure and tree mortality were noted, consumption of the organic layer was relatively high and the DMC and DC numerical ratings were categorized as extreme or high risks. Site characteristics, in particular \nmicrotopography and vegetation, were significant factors in accounting for the amount of consumption of these organic layers. The efficacy of the FWI fire risk ratings for \nindicating organic layer consumption was bolstered when coupled with these site characteristics. Additionally, planning smoldering fires for forest ecological or \nmanagement goals is facilitated by the FWI values.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".