Planning for Shoreline Response to Spills in Arctic Environments
Bibliographic record
Abstract
ABSTRACT The Arctic coasts present three unique shoreline types that are common in North America and Eurasia, but that are not found in lower latitudes or in the southern hemisphere: tundra cliffs, peat shorelines, and inundated low-land tundra. Tundra cliffs range in character from ice-rich exposures that are dominated by rapid thermo-erosional processes to high (10–15 meters) sediment-rich cliffs that may be eroded by slumping or basal sapping. One product of this rapid erosion of the tundra is to produce large volumes of peat and in many sections these form the dominant shore-zone material. In low-lying areas the flooding of the tundra has produced extremely complex shoreline configurations characterized by the elevated rims of patterned ground. These unique arctic shore types present different sets of challenges for shoreline cleanup and treatment and have been included in the U.S. marine oil spill response guide published in 2001 by API, NOAA, USCG, and USEPA and several specialized Arctic response manuals published recently by Environment Canada. A low-altitude aerial videotape survey in 2001 produced continuous images of the mainland and barrier island coasts of the Alaskan Beaufort and Chukchi Sea coasts from the Canadian border to Point Hope, used to map the shore types as part of a mapping project for the Minerals Management Service. The mapping revealed that the three arctic shoreline types are present on more than half (54 per cent) of the coast between the Canadian border and Point Hope.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".