Bibliographic record
Abstract
The objective of the pipeline encroachment monitoring project was to demonstrate the feasibility of using Earth observation (EO) satellites for early detection of third-party encroachment activities. Examples of encroachment activities include road construction, cable laying, farming, and residential or commercial development. Optical and synthetic aperture radar (SAR) images taken from space-borne platforms were evaluated for use in detecting encroachment activities. C-CORE and TransCanada Pipelines Ltd. conducted an encroachment field program near Whitecourt, Alberta. Various encroachment activities, principally involving heavy equipment, were staged and concurrent satellite-based optical (IKONOS) and SAR (RADARSAT) images acquired. These images were then analyzed for signatures of the encroachment events. A total of 22 multispectral and panchromatic IKONOS images and 14 RADARSAT SAR images were acquired. The IKONOS satellite proved to be effective in detecting smaller vehicles (for example, small trucks or Bobcat tractors) in areas that are free from cloud cover. RADARSAT proved effective in the detection of larger vehicles (for example, excavators or dump trucks), linear excavation, deforestation, and the provision of reliable coverage in all weather conditions.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".