Spatial and temporal variability of forest floor duff characteristics in long-unburned<i>Pinus palustris</i>forests
Bibliographic record
Abstract
Duff fires (smouldering in fermentation and humus forest floor horizons) and their consequences have been documented in fire-excluded ecosystems but with little attention to their underlying drivers. Duff characteristics influence the ignition and spread of smouldering fires, and their spatial patterns on the forest floor may be an important link to the heterogeneity of consumption observed following fires. We evaluated fuel bed characteristics (depths, bulk densities, and moisture) of duff in a long-unburned longleaf pine (Pinus palustris Mill.) forest and corresponding spatial variation across 100to 103m scales. Fermentation and humus horizon depths both varied (∼100% coefficient of variation) but with moderate to strong spatial autocorrelation at fine scales. Fermentation bulk density varied less than humus bulk density, which varied considerably at fine scales. Fermentation horizons held more moisture (average 49%–172%) and were much more variable than humus following rainfall, which remained stable and relatively dry (average 28%–62%). Humus moisture was moderately autocorrelated at fine scales, but fermentation moisture was highly variable, showing no evidence of spatial autocorrelation under dry, intermediate, or wet conditions. Observations from this study highlight the underlying spatial variability in duff, informing future sampling and fire management efforts in these long-unburned coniferous forests.
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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 | 0.000 | 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 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".