Modeling the effects of varied forest management regimes on carbon dynamics in jack pine stands under climate change
Bibliographic record
Abstract
Climate change and its potential effects on ecosystems justify the need to implement forest management strategies that increase carbon (C) sequestration. A process-based model, TRIPLEX-Management, was used to investigate how to increase C sequestration within managed jack pine (Pinus banksiana Lamb.) forests. The simulations included a constant climate scenario and two climate change scenarios generated from the Coupled Global Climate Model (CGCM 3.1). A total of 36 forest management scenarios (a control where no forest management occurred, five varied rotation length harvesting-only regimes, and combinations of six thinning regimes and five rotation lengths) were simulated under each climate scenario for nine sites characterized by stocking levels from 0.3 to 0.7. A significant increase in C sequestration was generated under the climate change scenarios compared with those under constant climate. Mean annual net ecosystem productivity (NEP) varied with rotation length, but was not changed by precommercial thinning. Future studies should consider life cycle analysis of harvested wood products as in this study they were assumed to be a permanent C sink. Climate warming might enhance limited positive effects of forest thinning on C sequestration. Shortening rotation length from 70–80 years to 50 years might enhance NEP, increase wood production, and decrease the risk of climate change impacts on jack pine 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".