Evaluation of Approaches to Depicting First Nations, Inupiat and Inuvialuit Environmental Information in GIS Format: Options for the Handling of Spatial Information in the Arctic Borderlands Ecological Knowledge Co-Op Database
Bibliographic record
Abstract
As the pace of climate change continues to accelerate in the North, traditional environmental knowledge systems are increasingly recognized by researchers, land use planners, government agencies, policy-makers and indigenous peoples as important contributors to environmental impact and climate change assessment and monitoring. Increasing temperatures, melting glaciers, reductions in the extent and thickness of sea ice, thawing permafrost and rising sea levels all provide strong evidence of increasing temperatures in the Arctic. This warming climate has the potential to change migration patterns, the diversity, range, and distribution of animal and plant species, and increase contaminants in the food chain from atmospheric transport of organic pollutants and mercury, thus raising concerns regarding the safety of traditional foods. Since 1996, the Arctic Borderlands Ecological Knowledge Co-op (ABEKC) has systematically recorded First Nations, Inupiat and Inuvialuit observations of landscape changes in the lower Mackenzie, Northern Yukon and eastern Alaska. Time-series data (regarding berry, caribou, fish, weather, ice and snow, plants, and other animal observations) have been obtained through annual interviews with the most active fishers, harvesters and hunters in the communities of Aklavik, Arctic Village, Fort McPherson, Kaktovik, Old Crow, and more recently, in Inuvik, Tsiigehtchic, and Tuktoyaktuk. An evaluation of the spatial utility of the ABEKC database and the many steps that are involved in the collection, storage, and organization of the Co-op’s data was documented. The ABEKC database provided an excellent opportunity to explore the problem of depicting complex qualitative information on northern landscape change in an intelligible GIS format. Initial attempts to develop the database in spatial format were critically evaluated and recommendations were provided in order to explore whether the data gathering and subsequent mapping process can be improved, whether more useful information can be obtained from the data, and to ensure the proper handling of the data in future years.
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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 | 0.037 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".