Forest carbon stocks in Newfoundland boreal forests of harvest and natural disturbance origin I: field study
Bibliographic record
Abstract
Quantification of stand and forest C stocks in response to different disturbances is necessary to develop climate change mitigation strategies and to evaluate forest C accounting tools. Live tree, dead tree, woody debris (WD), stump, buried wood, and organic and mineral soil C stocks are described in chronosequences of black spruce ( Picea mariana (Mill.) BSP) (harvest and fire origin) and balsam fir ( Abies balsamea (L.) Mill.) (insect and harvest origin). The largest C stocks were found in mineral soil (≤179 Mg·ha–1), organic soil (≤123 Mg·ha–1), and live tree (≤93 Mg·ha–1) pools. Live tree C changed predictably with disturbance history and time since disturbance, increasing with forest age. Regeneration growth slowed under snags. Thinning accelerated production of larger trees but reduced site live tree C. Dead tree and WD C were temporally dynamic and strongly influenced by disturbance history and time since disturbance, but abundances in differently disturbed forests converged at low levels 40–60 years after disturbance. Only immediately following natural disturbances were there large amounts of snag C (26–30 Mg·ha–1). WD C was relatively abundant <3 years after harvesting (15–17 Mg·ha–1) and 31–36 years after natural disturbance (9 Mg·ha–1). Buried wood stocks were small, but comparable with WD stocks in some 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 | 0.001 | 0.001 |
| 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".