Patterns and Drivers of Change in the Normalized Difference Vegetation Index in Nunavik (Québec, Canada) over the Period 1984–2020
Bibliographic record
Abstract
Altered temperature and precipitation regimes associated with climate change generally result in improved conditions for plant growth. For Arctic and sub-Arctic ecosystems, this new climatic context promotes an increase in primary productivity, a phenomenon often referred to as “greening”. Although this phenomenon has been widely documented at the circumpolar scale, little information is available at the scale of plant communities, the basic unit of the Arctic and sub-Arctic landscape mosaic. The objectives of this study were (1) to quantify the variation of NDVI within the different plant communities of Nunavik (Québec, QC, Canada) in order to identify which ones contributed the most to the greening and (2) to identify the climatic and biophysical drivers of the greening. To do so, we used Landsat imagery to produce a time series of summer NDVI for the period 1984 to 2020. A fine-resolution map of Northern Québec vegetation was then overlaid on the time series of NDVI imagery and on maps of surficial deposits, topography, and gridded climate data to obtain information at the plant community level. We found that greening was more important in shrub-dominated communities, particularly near the tree line. Summer temperature, fall and winter precipitation, and surficial deposits were identified as drivers of greening. Through utilizing detailed vegetation maps to accurately quantify changes in Nunavik’s plant communities, this study provides valuable insights into the dynamics of the region’s ecosystem under rapid climate change.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".