Process and patterns of duff consumption in the mixedwood boreal forest
Bibliographic record
Abstract
Postfire tree recruitment in the boreal forest is restricted to patches from which the duff (organic layer) has been removed by fire. Duff consumption occurs by smoldering combustion, propagation of which is determined by bulk density, moisture content, and depth. This study investigated interactions among these factors, their spatial distribution, and spatial patterns of duff consumption in two wildfires. A hypothesized positive relationship between moisture content and depth was supported by a laboratory study. Duff characteristic data were collected from two burns and comparable unburned areas of mixedwood forest in western Canada to describe and explain patterns of duff consumption within and between top-slope Pinus banksiana Lamb. Picea mariana (Mill.) BSP stands and lower slope pure stands of Picea mariana on glaciofluvial hillslopes. In unburned stands, bulk density did not differ significantly between stands, while depths were significantly greater in Picea stands. With moist duff, consumption was significantly greater in Picea than in PinusPicea stands as depth limited smoldering in the thin duff of PinusPicea stands. With dry duff, smoldering was propagated regardless of depth resulting in no differences between stand types. The spatial correlation between burned patches and fire-killed trees was explained by within-stand spatial variation in duff moisture due to precipitation interception by tree crowns.
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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 | 0.000 |
| 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) | 0.000 | 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".