Mapping Coastal Information across Canada's Northern Regions Based on Low-Altitude Helicopter Videography in Support of Environmental Emergency Preparedness Efforts
Bibliographic record
Abstract
Wynja, V.; Demers, A.-M.; Laforest, S.; Lacelle, M.; Pasher, J.; Duffe, J.; Chaudhary, B.; Wang, H., and Giles, T., 2015. Mapping coastal information across Canada's northern regions based on low-altitude helicopter videography in support of environmental emergency preparedness efforts.In the face of increasing economic opportunities in Canada's northern regions, the need to improve our state of preparedness for oil spill–related emergencies is critical. While significant efforts have been put toward documenting baseline coastal information across Canada's southern regions, there is a large information gap regarding Arctic shorelines. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Georeferenced high-definition videography was collected during the summers of 2010 to 2012 along coastlines within six study sites across the Canadian Arctic. Detailed information describing the upper intertidal, supratidal, and backshore zones was extracted from the video and entered into a geospatial database using a data collection form. This information was used to delimit and map alongshore segments in the upper intertidal zone. Almost 15,000 km of northern shorelines were mapped, including 25 shoreline types based on the upper intertidal zone. This information will feed into a larger ongoing project focused on Arctic coastal ecosystems and oil spill response planning should the need arise.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".