Refining estimates of U.S. reforestation opportunities for climate mitigation: updated algorithms and analyses
Bibliographic record
Abstract
Reforestation has substantial potential for carbon sequestration in the conterminous United States. My inspection of the results of a recent novel study of potential reforestation in the US revealed potentially important issues in some locations related to harvested forests and productive cropland. Here, I describe a study that refined the methods to address these issues and included an assessment of the albedo impact of reforestation. One update to improve identifying forest harvest caused substantial decreases in sequestration potential. In addition, five different approaches were developed for updating the estimate of marginal cropland, allowing for variability in the definition of “marginal”. Sequestration potential increased or decreased, depending on approach. Because these updates were somewhat offsetting, the summed sequestration potential for the US was about 86% of the original results. At state and finer scales, substantial increases and decreases occurred. Geographic patterns were similar to the original study, with highest potential in the eastern US and coastal western US. These areas of high sequestration would be less affected by the modified albedo following reforestation than other areas. The refinements described here likely result in greater accuracy and higher confidence in some locations and situations of potential reforestation actions in the US.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".