Dead Wood in High-Boreal Labrador Black Spruce Forests – Buried And Forgotten?
Bibliographic record
Abstract
Dead wood (DW), and in particular woody debris (WD), is an important component of the forest C cycle. In cool and wet climates, where microbial activity is restricted and moss growth is vigorous, large amounts of WD can be buried, i.e. overgrown by moss. Abundance, size, and decay class of DW buried in the organic layer were assessed in 15 stands of black spruce (Picea mariana (Mill.) BSP) in Labrador: 3 old-growth stands, and 12 stands regrown following clearcut harvesting (1970-72, 1989, and 2005) or wildfire (1985). Field measurements were based on Line Intersect Sampling and the Canadian National Forest Inventory Ground-plot Protocol. Harvested, burned, and old-growth sites contained 5.8ñ9.6, 4.7, and 18.2ñ37.3 Mg C ha-1 of buried dead wood (BW), respectively. Old-growth BW-C stocks largely exceeded total aboveground DW-C stocks (12.0 Mg C ha-1), indicating accumulation and/or preservation over long time periods. Stand-replacing fires, the predominant regional natural disturbance, burn only a portion of the organic layer and thus wood buried in it, potentially not interrupting the accumulation of BW over several stand generations. BW in old-growth sites was mainly in decay class 4 and 5, but decay class 2 and 3 BW contributed ~30% of total BW-C. A considerable portion of WD is hence buried before reaching more advanced stages of decay. Following burial, decay rates likely slow down considerably due to cold and moist conditions. BW accumulation appears to depend on a combination of climate (e.g., temperature, precipitation), micro-topography (e.g., drainage), ground vegetation (e.g., moss growth), and stand disturbance history (e.g., fire intensity and return interval). Excluding BW from DW inventories in cool and moist coniferous forests with a vigorous moss layer and long fire-return intervals such as found in high-boreal Labrador or coastal Scandinavia can result in massive underestimates of DW-C stocks.
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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) | 0.000 | 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 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".