NDVI variation and its relation to climate in Canadian ecozones
Bibliographic record
Abstract
Parks Canada began the Northern Satellite Monitoring Program in 1997, with the objective of tracking large‐scale vegetation variation in Canadian ecosystems and helping land managers to develop appropriate management practices in response to climate change. Under this program, a sequence of 10‐day composite Advanced Very High Resolution Radiometer (AVHRR)‐derived Normalized Difference Vegetation Index (NDVI) data from 1985 to 2007 was examined to study seasonal and inter‐annual relationships between vegetation and climate data over Canadian ecosystems using statistical and wavelet analysis. Statistical analysis showed that temperature was the principal driver for seasonal variability in greenness, explaining more than 70 percent of seasonal variation in vegetation for most Canadian ecozones. In comparison with temperature, the relationships between NDVI and precipitation were weaker but still significant. Maximum annual NDVI showed increasing trends in Canadian ecozones during the study period, although increasing rates were spatially heterogeneous. Wavelet analysis confirmed that inter‐annual variation in NDVI was different at two ecozones in Canada. NDVI variation in the Northern Arctic was significant at scales of 3–4 years from 1997 to 2001, which was associated with temperature and precipitation variation. Comparatively, NDVI variation in the Boreal Shield was significant at scales of 5–8 years from 1991 to 1999, but did not correspond with climate variation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".