Spatial-temporal-thematic assimilation of Landsat-based and archived historical information for measuring urbanization processes
Bibliographic record
Abstract
Measuring urbanization and assessing its impacts require long-term records of land changes, which cannot typically be provided from a single information source. A prerequisite to the creation of a reasonably consistent national information database on urban growth was the development of a robust methodology to assimilate land information from diverse sources. This method was applied to assimilation of two information sources, the Canadian Urban Land Use Survey (CUrLUS) and the Canada Land Use Monitoring Program (CLUMP). CUrLUS consists of a suite of contemporary thematic maps derived from satellite images while CLUMP information was extracted through conventional visual interpretation of aerial photography. In the process of generating integrated temporal series, the compatibility between the two information sets was assessed. The application of the assimilation methodology has led to generation of reasonably consistent urban land-cover and land-use change information for major Canadian urbanized areas spanning a 35-year period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".