Session: Urban Development and Growth Patterns
Bibliographic record
Abstract
While Canada ranks second in the world in terms of national land area, its most productive arable land is limited in extent. These lands are also under pressure due to rapid urbanization, especially in the high growth areas of southern Ontario, the Calgary-Edmonton corridor and the lower Fraser River valley of British Columbia. Currently, a program is underway in the Earth Sciences Sector of Natural Resources Canada to quantify urban transportation sustainability in support of energy policy-makers. This work includes the creation of the Canadian Urban Land Use Survey (CUrLUS), a series of land-cover/land-use (LCLU) maps, derived in part from Landsat Thematic Mapper imagery, for all Canadian cities with populations in excess of 200,000. These LCLU maps have potential application beyond transportation issues. To study land conversion impacts during the period 1966-2001, it has been necessary to assimilate this information with historic land use sources from other federal initiatives including the Canada Land Use Mapping (CLUMP) program and the Canada Land Inventory (CLI). This paper addresses assimilation issues through an assessment of the consistency of these information sources leading to a rationalization of their class legends and spatial resolution differences. _________________________ 1.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.252 | 0.036 |
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".