A Case Study of Carbon Sequestration Potential of Land Use Policies Favoring Re-growth and Long-term Protection of Temperate Forests
Bibliographic record
Abstract
There is a traditional view suggesting forests remove carbon dioxide from the atmosphere (Pregitzer & Euskirchen, 2004), but they cease to serve as a carbon sink as they fully mature (Odum, 1969). Recent modeling of old-growth forests indicate they continue to serve as a “net sink” of carbon even after maturity (Carey, Sala, & Callaway, 2001; Zhou, 2006), sequestering an average of 2.4 +/- 0.8 tC ha-1 yr-1 (tC = metric tons of carbon; ha = hectare; yr =year), identifying a ratio of heterotrophic respiration (Rh) to net primary production (NPP) of approximately 0.65 +/- 0.02 (Luyssaert, 2008). These figures show the strongest correlation amongst temperate forest regions. Two calculations are made using the carbon sequestration average. One is made identifying the amount of carbon sequestered through a small-scale land protection organization, showing a net carbon sequestration of approximately 224 metric tons of carbon per year. The other is based on the amount of land required to offset current anthropogenic emissions of carbon in the global carbon budget, showing approximately 235 million hectares of new forest growth would be required to offset current global anthropogenic emissions. One implication of these calculations is the traditional assumption of carbon neutrality increasing with age (Magnani, 2007) is incorrect, suggesting re-growing forests may be a favored policy choice for continued carbon sequestration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".