Seasonal climate drivers of peak NDVI in a series of Arctic peatlands
Bibliographic record
Abstract
Changes in plant cover and productivity are important in driving Arctic soil carbon dynamics and sequestration, especially in peatlands. Warming trends in the Arctic are known to have resulted in changes in plant productivity, extent and community composition, but more data are still needed to improve understanding of the complex controls and processes involved. Here we assess plant productivity response to climate variability between 1985 and 2020 by comparing peak growing season NDVI (Normalised Difference Vegetation Index data from Landsat 5 and 7), to seasonal-average weather data (temperature, precipitation and snow-melt timing) in nine locations containing peatlands in high- and low-Arctic regions in Europe and Canada. We find that spring (correlation 0.36 for peat dominant and 0.39 for mosaic; MLR coefficient 0.20 for peat, 0.29 for mosaic), summer (0.47, 0.42; 0.18, 0.17) and preceding-autumn (0.35, 0.25; 0.33, 0.27) temperature are linked to peak growing season NDVI at our sites between 1985 and 2020, whilst spring snow melt timing (0.42, 0.45; 0.25, 0.32) is also important, and growing season water availability is likely site-specific. According to regression trees, a warm preceding autumn (September-October-November) is more important than a warm summer (June-July-August) in predicting the highest peak season productivity in the peat-dominated areas. Mechanisms linked to soil processes may explain the importance of previous-Autumn conditions on productivity. We further find that peak productivity increases in these Arctic peatlands are comparable to those in the surrounding non-peatland-dominant vegetation. Increased productivity in and around Arctic peatlands suggests a potential to increased soil carbon sequestration with future warming, but further work is needed to test whether this is evident in observations of recent peat accumulation and extent.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".