Optimal on- and off-site forest carbon sequestration under existing timber supply constraints in northern New Brunswick
Bibliographic record
Abstract
We describe a procedure to maximize carbon (C) sequestration and apply it to a 428 000 ha industrial forest management area in northern New Brunswick, Canada. Stand-specific C yield tables and C residency periods in harvested wood products were used as inputs to a linear programming model to maximize on- and off-site C sequestration in forest land. Five management scenarios were evaluated. A scenario that maximized on-site forest C sequestration for 80 years, respecting “business-as-usual” harvest constraints, projected an extra 3 t C·ha–1 across the forest management area compared with the business-as-usual scenario, with net C storage potential (forest C + forest C in products – emissions produced from decayed wood products) resulting in approximately 1 Mt C. A scenario to double softwood harvest led to a projected decrease in the forest C pool by approximately 5 t C·ha–1 from 2007 to 2082 and overall storage decrease of almost 2 Mt C from the base run. Other scenarios to increase or decrease harvest volumes by 10% resulted in overall C storage increases of 1.6 Mt C and almost 2.7 Mt C, respectively, above the base run. All scenarios resulted in net sinks of C after the 80 year simulation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".