How we value the Coast - Arctic Coastal Ecosystem Services on Qikiqtaruk, Canada
Bibliographic record
Abstract
Arctic coasts are strongly impacted by global climate change. While coastal erosion is one of the most visible consequences, changes in the supply and demand of ecosystem services (ES) are also having an increasingly significant effect on the communities living along these coasts. While the concept of ecosystem services is relevant, it is underutilized, particularly in Arctic regions. In the scope of my PhD project, we will access in the first part the supply of ES on Qikiqtaruk (Herschel Island), Canada followed by stakeholder interviews to quantify the demand of coastal ES. This project will involve measuring physical and biological parameters, including permafrost and active layer temperatures, water quality (turbidity and salinity), and fish catches, with a particular focus on the condition of the fish. These measurements will help characterize the ecosystems present on the island. Combining these results with the stakeholder interviews and participatory mapping will provide a detailed overview of the use of, and need for, ecosystem services on the Arctic coast of Qikiqtaruk. The special role of the island as a Territorial Park, as well as its importance for traditional land use and education, will be considered, and we will try to co-produce outputs that can be used by the Herschel Island–Qikiqtaruk Rangers and inform potential policy decisions around the Territorial Park. The future direction of this project will be shaped by consultation meetings with local stakeholders during the upcoming field season. One potential approach is to extend the assessment of coastal ecosystem service supply and demand to the wider Inuvialuit Settlement Region in order to address these issues on a regional scale.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".