Arctic Monitoring: A Remote Sensing Analysis of Former Wellsites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it