Disturbance history affects dead wood abundance in Newfoundland boreal forests
Bibliographic record
Abstract
Dead wood (dead standing tree (snag), woody debris (WD), buried wood, and stump) abundance was estimated in Newfoundland balsam fir (Abies balsamea (L.) Mill.) and black spruce (Picea mariana (Mill.) BSP) forests regrown following natural and anthropogenic disturbances. Although harvesting left few snags standing, natural disturbances generated many snags. Most were still standing 2 years after natural disturbance, but almost all had fallen after 33–34 years. Snag abundance then increased in stands aged 86–109 years. Natural disturbances generated little WD 0–2 years following disturbance. Harvesting, however, immediately generated large amounts of WD. Thirty-two to forty-one years following disturbance, most harvesting slash had decomposed, but naturally disturbed sites had large amounts of WD from collapsed snags. Harvested sites contained less WD 32–72 years following disturbance than naturally disturbed sites. Amounts of WD in black spruce regrown following harvesting and fire converged 63–72 years following disturbance, despite significant initial differences in WD quantities, diameter distribution, and decay classes. Abundance of WD increased from sites regrown 32–72 years following disturbance to older sites. Precommercial thinning had a minor impact on dead wood stocks. Stumps contained minor biomass. Buried wood and WD biomass were equivalent at some sites.
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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 | 0.001 |
| 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 | 0.001 |
| 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".