Bibliographic record
Abstract
NAP on ‘deep decarbonization’. Chatham House disses ‘ineffective’ CO2 removal tech. Northern Lights CCS on NCS. DoE funds LANL’s DigiMon program. IOGP calls on EU to scale-up CCS. OGCI CCUS KickStarter. MIT on uncertain ‘bridge fuel’ role for natural gas. Project Canary, Payne Institute to monitor Colorado fugitive emissions. BP deploys gas cloud imaging. Gaffney-Cline on use of Stanford’s OPGEE carbon monitor. Canada funds e-dump valve actuator development. Energy Futures Initiative report on ‘Clearing the air’. US EPA relaxes ‘redundant’ Obama-era regulations. ONE Future members reduced methane intensity. Yokogawa/KBC on end game strategy for oil and gas. ABB on climate change and downstream. Gaffney-Cline on CCUS status. Oxy Low Carbon Ventures to study CCUS at Holcim cement works. Direct air capture by MIT. BP funds FiniteCarbon offsets. Total Carbon Neutrality Ventures fund. ISO environmental monitoring standard. IOGP environmental genomics. Geology and decarbonization. Total ditches US association membership. MIT on climate financial disclosure. The (sustainable) world according to GARP. BBC Radio 4 on CCS, ‘net zero’ and the survival of the oil industry.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.413 | 0.227 |
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".