The western North American forestland carbon sink: will our climate commitments go up in smoke?
Bibliographic record
Abstract
Pathways to achieving net‐zero and net‐negative greenhouse‐gas (GHG) emission targets rely on land‐based contributions to carbon (C) sequestration. However, projections of future contributions neglect to consider ecosystems, climate change, legacy impacts of continental‐scale fire exclusion, forest accretion and densification, and a century or more of management. These influences predispose western North American forests (wNAFs) to severe drought impacts, large and chronic outbreaks of insect pests, and increasingly large and severe wildfires. To realistically assess contributions of future terrestrial C sinks, we must quantify the amount and configuration of stored C in wNAFs, its vulnerability to severe disturbance and climatic changes, costs and net GHG impacts of feasible transitions to conditions that can tolerate active fire, and opportunities for redirecting thinning‐derived biomass to uses that retain harvested C while reducing emissions from alternate products. Failing to adopt this broader mindset, future forest contributions to emission targets will go up in smoke .
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".