Reduction potentials and abatement costs for methane emissions associated with compromised wellbore sealing systems in Russian oil wells
Bibliographic record
Abstract
Artificial intelligence APB Annular Pressure Build-up APG Associated petroleum gas API American petroleum Institute BC OGC Columbia Oil and Gas Commission BU Bottom Up approach CBLs Cement bond logs DHSV Downhole Safety Valves ECCC Environment and Climate Change Canada EIA International Energy Agency EPA U.S. Environmental Protection Agency GHG Green House Gases GM Gas migration GOR Gas/oil ratio HPT High-precision temperature ID Internal diameter IMEO International Methane Emissions Observatory ISO International Organization for Standardization LDAR Leak detection and repair NEA Norwegian Environment Agency NIR National Inventory Report NOC National oil company OJSC An open joint-stock company P&A Plugging and abandonment SCP Sustained casing pressure SCVF Surface casing vent flow SCVT Surface casing vent test SDS Sustainable Development Scenario SINTEF Norwegian: Stiftelsen for industriell og teknisk forskning SMEs Small and medium-sized enterprises SNL Spectral noise logging TD Top Down approach UNFCCC
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".