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 SAR Interfeometry (InSAR) provides high precision ground displacement measurements remotely, using Synthetic Aperture Radar (SAR) images acquired from satellites. Thanks to its effective provision of extensive information over wide areas with high acquisition frequency, InSAR monitoring is used routinely in the management of numbers of Enhanced Oil Recovery (EOR) projects. These include heavy oil Cyclic Steam Stimulation (CSS), Steam Flooding (SF) and Steam Assisted Gravity Drainage (SAGD) in Alberta and California. Steam injection recovery is generally operated in shallow reservoirs with low caprock thickness, where measuring the surface effects of pressure variations at depth is extremely useful to assess steam chest expansion and enhance safety. InSAR monitoring provides low-cost effective measurements over large areas and is capable of highlighting zones of excessive pressure or subsidence, as well as to control the integrity and safety of operations and infrastructures. This paper presents an overview of InSAR technologies and their recent enhancements. Some examples of InSAR application in EOR heavy oil projects are reported in order to highlight the advantages offered by these monitoring techniques in reservoir management and recovery optimization.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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