An inventory and morphometric analysis of British Columbia glaciers, Canada
Bibliographic record
Abstract
Abstract We describe an automated method to generate an inventory of glaciers and glacier morphometry from a digital topographic database containing glacier boundaries and a digital elevation model for British Columbia, Canada. The inventory contains over 12 000 glaciers with a total cumulative area that exceeds 25 000 km2, based on mapping from aerial photographs circa the mid-1980s. We use the inventory to examine dimensional characteristics among glaciers, namely the scaling relations between glacier length, width and area. Glacier length is a good predictor of glacier area, and its predictive ability improves when glaciers are stratified by the number of up-valley accumulation basins. The spatial pattern of glacier mid-range altitude parallels glaciation limits previously mapped for British Columbia and similarly reflects large-scale controls of orographic precipitation and continentality. The inventory is also used to refine models that relate glacier mid-range and terminus altitudes to regional position, aspect and, in the case of terminus altitudes, an index of glacier shape. Relations between glacier altitude limits and controlling spatial and topographic factors are used to make further climatic and mass-balance inferences from the glacier inventory.
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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 | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".