Arctic Monitoring: A Remote Sensing Analysis of Former Wellsites
Bibliographic record
Abstract
Abstract Numerous exploratory wellsites were established in Canada's Arctic during the second half of the 20th century and were subsequently closed. Due to the logistic challenges of monitoring such sites through conventional approaches, the operator engaged the service provider to conduct a study using remote sensing techniques and high-resolution optical imagery on several closed wellsites (7 sites on-shore) in the Mackenzie River Delta of Northwest Territories, Canada. The project focused on demonstrating the ability to track changes in site conditions (retrospectively), distinguishing cyclic from progressive changes, and evaluating the potential cost for routine site monitoring at different intervals. Available sources of optical imagery were used; from 1 m IKONOS to 0.5 m WorldView-2, with dates ranging from 2002 through 2014. The optical analysis utilized the Normalized Difference Vegetation Index (NDVI) ratio of near infrared and red bands to provide an index of biomass density. A variety of processing techniques and analyses were performed that focused on four major areas: relative water levels, vegetation health, condition of infrastructure, and the proximity of nearby receptors. Synthetic Aperture Radar (SAR) imagery from RADARSAT-2 was also used successfully to detect pilings that were not visible in the optical imagery.
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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 | 0.002 | 0.002 |
| 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".