Missing forest cover gains in boreal forests explained
Bibliographic record
Abstract
Abstract A recent global study reported a net difference between areas of forest cover loss and of forest cover gain of about 3.6% of total forest area across the boreal biome, and of 5.6% for Canada, over a 12‐yr period. Net losses of this magnitude should be of concern given the importance of this biome in global biogeochemical cycles linked to climate change. Our analysis for Canada fails to support these results and suggests that post‐harvest recovery of tree cover is generally strong, while post‐fire recovery of tree cover is weaker but nevertheless prevalent. We find that current large area remote sensing methodologies can fail to properly recognize post‐disturbance recovery from non‐forest to forest status in low‐productivity boreal forests when using short time series. With climate change and human impacts intensifying around the world, it is urgently important to be able to reliably distinguish temporary forest cover loss followed by naturally slow recovery from forest decline requiring policy action. The analysis was in large part based on the new Canada Landsat Disturbance product in which fires and harvest since 1984 are mapped at 30‐m resolution ( https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".