Characterizing spatiotemporal environmental variation throughout Ontario, Canada, using remote sensing derived-indicators
Bibliographic record
Abstract
Ecosystems are naturally variable. This variability can be due to the inherent land cover, topography, seasonality, and natural variations in climate. Ecosystem variability can also extend beyond this natural range due to disturbances such as fire, harvesting, land conversion, insect infestation, and increasingly may be due to extremes in climate (e.g. snow, ice storms, flooding, rainfall, temperature fluctuation). Differentiating disturbances from natural variability and understanding vegetation productivity changes resulting from disturbances are of high importance for ecosystem management. To capture ecosystem variation over the province of Ontario, Canada, we utilised a series of ten‐day composites of Medium Resolution Imaging Spectroradiometer (MERIS) fraction of Photosynthetically Active Radiation (fPAR) over a 6 year period. We investigated changes in the variations in fPAR derived vegetation indices (annual productivity, degree of vegetation seasonality and vegetative perennial cover) using a non‐parametric statistical test. Results indicated that considerable changes in vegetation productivity are occurring in Eastern Ontario as well as in other more localized regions in northern Ontario. Using a range of auxiliary information on fire disturbance, land cover, distance to nearest road and city, topography and protected areas, we provide explanations as to the possible drivers behind this variability.
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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 | 0.003 |
| 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".