Bibliographic record
Abstract
EPA $27 billion GHG reduction fund. AspenTech’s Operational Insights. Iconic Air’s Emissions Intelligence. UK’s Climate Resilience Demonstrator. EU low-carbon Model Explorer. Envana Catalyst. New Mexico OK’s Flogistix. PetroMasila uses flare gas. Gas Liquids Engineering. Azuli launched. Inmarsat: ‘ESG reporting not believable’. Carbon Disclosure Project: Only 12 companies out of 18,700 merit ‘Triple A’. Canada ends for fossil fuel. TSOR teams with Eion on CO2 removal. Chevron, Baseload Capital team on geothermal. EU funds Energy Dome’s CO2 battery. Pason Systems’ sustainability report. Graforce a winner in Petronas ‘Race2Decarbonise’. IEAGHG’s CO2DataShare. Kontrol Technologies, LNG emissions and GIIGNL framework. MiQ certifies CF Industries’ natural gas. Project Canary certifies Hyperion Midstream operations, teams with Xpansiv. Occidental/1PointFive kicks of DAC in Permian Basin. CanERIC, PTAC and Canada’s CRIN. GeothermEx audits Deep Earth Energy. TOG Enterprise Architecture and sustainability. API/Ipieca Guide to sustainability reporting in oil and gas.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.356 | 0.218 |
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".