Two distinct waves of greening in northeastern Canada: summer warming does not tell the whole story
Bibliographic record
Abstract
Abstract Arctic vegetation cover has been increasing over the last 40 years, which has been attributed mostly to increases in temperature. Yet, the temporal dimension of this greening remains overlooked as it is often viewed as a monotonic trend. Here, using 11 year long rolling windows on 30 m resolution Landsat data, we examined the temporal variations in greening in north-eastern Canada and its dependence on summer warming. We found two significant and distinct waves of greening, centred around 1996 and 2011, and observed in all land cover types (from boreal forest to arctic tundra). The first wave was more intense and correlated with increasing summer temperature while no such relation was found for the weaker second wave. More specifically, the greening lasted longer at higher elevation during the first wave which translates to a prolonged correlation between greening and summer warming compared to low-altitude vegetation. Our work explored a forsaken complexity of high latitude greening trends and associated drivers and has raised new questions that warrant further research highlighting the importance to include temporal dimension to greening analyses in conjunction with common spatial gradients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".