Deadwood abundance in Labrador high-boreal black spruce forests
Bibliographic record
Abstract
Deadwood (woody debris (WD), standing dead trees (snags), stumps, and buried deadwood) abundance was estimated in Labrador humid high-boreal black spruce (Picea mariana (Mill.) BSP) forests regrown following natural and anthropogenic disturbances. Aboveground deadwood (DW) abundance in Labrador was similar to values observed in other boreal forests experiencing drier or warmer climates. Clear-cut harvest generated large amounts of WD, which had almost completely decomposed 34–36 years following harvesting, with a fitted volume reduction rate of –0.058 year –1 . Total WD in all harvested stands was composed of predominantly <10 cm pieces, which should be included in DW inventories of disturbed coniferous boreal forests. Postfire WD likely peaked ∼20 years following disturbance, as a result of the collapse of snags, and was dominated by large amounts of medium-sized logs (10.0–19.9 cm). Buried DW stocks considerably exceeded total aboveground DW stocks in old-growth, middle-aged, and older harvested stands. Old-growth stands contained 179.3 m 3 ·ha –1 of buried DW, a vast amount indicative of long-term accumulation requiring significantly depressed rates of WD decomposition following burial. DW stocks could be significantly underestimated if buried DW is excluded from DW inventories in cool and moist coniferous forests with long fire-return intervals.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".