Investigating permafrost coastal erosion and the resulting nutrient input in the western \nCanadian Arctic
Bibliographic record
Abstract
Rapid environmental changes in the Arctic, including permafrost thaw and coastal erosion, are expected to have great impacts on the global climate system, on the ecosystem and on local communities. However, the magnitude at which these processes occur and their impacts is still not completely understood. This project focuses on the study of coastal erosion rates and the resulting organic carbon and nutrient release to the nearshore zone on Herschel Island and Yukon Coastal Plain in northwest Canada. The methodological approach involves field sampling and surveys, laboratory analyses and remote sensing. Fifteen permafrost cores from different ecological units were drilled during the expedition to Herschel Island in 2013. Additionally, training areas for ecological units were delineated with GPS. Core samples will be analysed for CNS, TOC, grain size and δ13C signature. Training areas will be used to produce a map of ecological units with supervised classification of RapidEye imagery. Maps for soil organic carbon and nitrogen content will be produced based on the assumption that soil organic carbon and nitrogen contents are homogenous within ecological units. Rates of coastal erosion will be calculated from spatially detailed DEMs from different years. Airborne LIDAR scanning of the Yukon Coast and Herschel Island with POLAR 5 was carried out in the summers 2012 and 2013. A high-resolution DEM based on LIDAR data will enable comparisons between short-term three-dimensional changes of the coastline and soil organic carbon and nitrogen release into the nearshore zone.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".