Modeling carbon sequestration with CO<sub>2</sub>Fix and a timber supply model for use in forest management planning
Bibliographic record
Abstract
Carbon (C) dynamics and forest management have become integrated in recent years, largely due to the Kyoto Protocol stipulating that forest C changes may be accountable in an emissions framework. A C stock modeling framework for forest managers is introduced in this paper. Empirical growth and yield models are used to develop sustainable timber supply for forest companies. These models use linear programming to solve the complex mathematical problem of timing and allocation of forest harvest and silviculture interventions. In this paper, we evaluated the effects of “business as usual”forest management versus management objectives to maximize C sequestration. Goal programming was used to minimize the deviation of two goals for C forest management: maximizing C in the forest, and maximizing the return on investment (net present value of forest timber products). Species-specific wood-to-C content conversion factors were used to parameterize the amount of C in forest stands on Canadian Forces Base Gagetown forest lands in New Brunswick, Canada. Goal programming reduced the loss of revenue associated with increasing C stocks in the forest. Partial harvesting and high valued end-products tended to increase C stocks and provided a higher return on investment in the simulations. Key words: Carbon stock modeling framework, forest, goal programming, partial harvesting, timber supply
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".