Bibliographic record
Abstract
There is abundance of oil sand resources in Alberta Basin,Canada,where the Athabasca area is the largest one.The oil-bearing intervals in Athabasca area are located in Cretaceous Mannville Group,and the objective formation is typical meandering channel deposit in the tidal environment.As a result of occurrence of biodegradation,the reservoir oil viscosity is up to 1×104-100×104 mPa·s.The main methods to develop the oil sands are open pit mining and drilling mining.SAGD technology belongs to drilling mining,whose mechanism is to inject high-temperature steam into the reservoir,so that the solid crude turns into flowing oil which flows into the wellbore to be produced.The expansion of steam chamber and the steam injection efficiency could be affected by the continuous reservoir thickness,interbed,gas/water layer distribution and the net pay length in horizontal section.In addition to porosity,shale content,oil saturation and resistivity,the recognition standard of net pay thickness for SAGD technology should also consider the situation about continuous reservoir,interbed and gas/water layer distribution.The oil sands reserve classification is determined by the reliability degree of geological background,the data acquisition and the development plan.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".