Woody debris volumes and carbon accumulation differ across a chronosequence of boreal red pine and jack pine stands
Bibliographic record
Abstract
Boreal forests are thought to store more than 30% of the world’s terrestrial carbon (C), much of it in the form of dead wood. Harvesting, stand transformation, and climate change the storage capacity of this carbon pool and improved quantification of C storage is needed to improve the accuracy and coverage of C accounting in Canadian forests. In this study, we compared wood volumes and C storage in coarse woody debris (CWD), fine woody debris (FWD), and standing dead wood (snags) in a 94-year chronosequence of jack pine (Pinus banksiana Lamb.) and red pine (Pinus resinosa Ait.) stands in the Sandilands Provincial Forest, southeastern Manitoba. In our data set of 20 jack pine and 17 red pine stands, jack pine stands supported higher volumes of CWD, snags, and sparsely distributed FWD than red pine stands. Mean CWD volume and C mass were, respectively, 18.6 m3·ha−1 and 2.6 tonnes (t)·ha−1 for jack pine and 11.3 m3·ha−1 and 1.1 t·ha−1 for red pine. Snag volumes and C mass were, respectively, 1.8 m3·ha−1 and 0.25 t·ha−1 for jack pine and 0.26 m3·ha−1 and 0.04 t·ha−1 for red pine. CWD loads in jack pine stands followed a U-shaped distribution with stand age, and snag loads in jack pine increased linearly with time. No such significant trends for CWD or snags were observed in red pine. Our results confirm that stand conversion from fire-origin jack pine to red pine plantations has the potential to significantly reduce and alter temporal patterns of dead wood accumulation across the landscape.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".