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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".