Incorporating Traditional Knowledge and Subsistence Mapping into the Arctic Environmental Response Management Application
Bibliographic record
Abstract
ABSTRACT Access to information from local and indigenous communities is vital to improving oil spill preparedness and response, and to ensuring efficient prioritization and protection of subsistence and culturally sensitive areas. The Environmental Response Management Application (ERMA®) is an online mapping tool that integrates both static and real-time data, such as Environmental Sensitivity Index maps, ship locations, weather, ocean currents, and more in a centralized format for environmental responders and decision makers. This allows for high-impact and fine-resolution visualization of data for solving complex environmental response and resource issues. As part of the overall ERMA project, baseline datasets have been collected from government sources, private corporations, universities, local entities, and non-governmental organizations (NGO). Arctic ERMA—a regional instance of the ERMA application—covers the U.S. high Arctic, with use in all of Alaska as well as internationally. To identify and gather Arctic-specific data, workshops were conducted in the Northwest Arctic Borough (NWAB), North Slope Borough (NSB), and Edmonton, Canada focusing on oil spill scenarios that could affect villages in each region, and developing prioritized datasets needed to support planning, response, and natural resource damage assessment (NRDA) work. As part of the overall ERMA project, baseline datasets have been collected from government sources, private corporations, universities, local entities and non-governmental organizations. Most of these datasets are publicly available. ERMA has been tested in Arctic drills and was used to support the USCG's “Arctic Shield” exercise, September 2013. Through this exercise, ERMA was able to incorporate onboard ship information, field-collected data, photos, sensor data and other scientific input collected during the USCG Cutter Healy cruise. The exercise identified some of the challenges the response community could face during a spill in the Arctic and the region's dependence upon local knowledge in successfully minimizing environmental effects and human-dimension impacts. This presentation will discuss collaborations and next steps.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".