No treeline advance over the last 50 years in subarctic western and central Canada and the problem of vegetation misclassification in remotely sensed data
Bibliographic record
Abstract
In this study we examined (1) whether there has been significant tree cover change over the period 1960–2010 in a 960,000 km2 subarctic study region in western and central Canada, and (2) the degree to which Global Forest Change (GFC) tree cover data agree with other datasets. We compared GFC tree cover to cover estimates from air photos (c. 1960), ground-level plot data (c. 1982–84), annotated low-level oblique photographs (c. 2005–09), and air photo footprints on the World Imagery Base Map (c. 2010). Tree cover changes since 1960 varied by physiographic and ecological regions. Afforestation was modest to non-significant depending on the region. We observed no evidence of northward tree migration. An increase in the areal extent of burned forests, mostly in areas south of the forest-tundra, was the largest change detected. We documented systematic discrepancies between our tree cover estimates and GFC data. GFC underestimates of tree cover typically occurred in areas of low tree density. Areas where GFC data overestimated tree cover were common, especially near the northern limits of trees and in areas dominated by dense or tall shrubs. Predictions of climate-driven vegetation response derived solely from remotely sensed data may not be reliable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".