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 Since the accidental discovery in the late 1800s of the benefits of injecting water into oil reservoirs to improve recovery, water has been the injection fluid preferred by the oil industry for use in recovery processes. Traditionally, waterflooding is considered an effective secondary recovery method for light and medium oil reservoirs. However, as production rates in these conventional reservoirs continue their decline, waterflooding is being considered, and used, more and more for the exploitation of more challenging heavy oil resources. Despite the unfavorable mobility ratio between injected water and more viscous heavy oils, many waterflood projects have been undertaken in heavy oil reservoirs around the world. However, the literature on heavy oil waterflooding is sparse. Those papers that have been published present a wide range of recovery factors for projects, and offer conflicting information on the theory and mechanisms involved in heavy oil waterfloods. The good news is that there is a long history in western Canada with waterfloods in heavy oil reservoirs, spanning more than 50 years. A vast amount of data and anecdotal information has been generated from these waterfloods. This could provide a key to raising the limits, in terms of reservoir conditions, under which waterflooding would be viable. In this paper we will discuss lessons learned after 50 years of waterflooding in heavy oil reservoirs, identify gaps in the application of the process, and speculate about what the future may hold as this technology evolves.
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) | 0.003 | 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