Ecoregionalization assessment: Spatio-temporal analysis of net primary production across Ontario
Bibliographic record
Abstract
An ecoregionalization can be defined as a partitioning scheme that captures landscape patterns by dividing an area into hierarchically nested ecounits based on similar physiographic and ecological characteristics. In this paper, we introduce new spatio-temporally explicit methods to characterize spatio-temporal variability of static, a priori defined, ecounits by their dynamic “spatio-temporal signatures” (STSs) and assess the strength of the ecoregionalization boundaries using this information. To analyze the spatial and temporal patterns of net primary productivity (NPP) at the ecozone, ecoregion, and ecodistrict levels of the National Ecological Framework of Canada (NEF) ecoregionalization, we compute a 15-year monthly series of NPP for Ontario at 8-km by 8-km resolution based on satellite images (NOAA-AVHRR) and a light-use efficiency model. At each level of the NEF hierarchy, within-unit homogeneity of the monthly, annual, and 15-year average NPP of ecounits is characterized by the Getis statistic, and between-unit heterogeneity of these variables is characterized by the boundary contrast (squared difference across the boundary). Similarities across the levels of the hierarchy are assessed by the sum-of-squared differences of monthly, annual, and 15-year average NPP of nested ecounits. Temporal trends of NPP per ecounit are measured using Kendall’s correlation coefficient. The seasonal and annual variations in the growing season, as captured by a time series of NPP aggregated to the ecodistrict, ecoregion, and ecozone level, are shown to vary across Ontario. These results indicate the potential of our spatio-temporal approach for ecoregionalization assessment based on dynamic and spatially distributed data.
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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 | 0.001 |
| 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".