Boreal Forest Carbon Sequestration Strategies: A Case Study of the Little Red River Cree First Nation Land Tenures*
Bibliographic record
Abstract
In this paper, creation of carbon offset and emission reduction credits are examined from the perspective of the Little Red River Cree Nation (LRRCN), a forest tenure holder in northern Alberta. Carbon credits are produced under three scenarios: (1) carbon uptake in forest ecosystems, with postharvest waste left on site; (2) carbon uptake in forests and products; and (3) carbon uptake in forests with harvested fiber used for energy production. A mathematical programming model is used to solve for the minimum prices that cause the LRRCN to include production of carbon credits in its forest management and post‐harvest processing strategies. If LRRCN is paid according to its costs of creating carbon credits, it will opt to use fiber for forest products as this provides the greatest earning potential. If LRRCN faces a fixed price for carbon credits, it will produce fiber for generating electricity in lieu of coal as this strategy has the lowest average cost. However, when costs of feedstock transportation and construction of a power plant are taken into account, carbon uptake in biomass and forest products turn out to be more competitive.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".