Continuous Energy Efficiency and Green House Gas Emission Surveillance and Control
Bibliographic record
Abstract
Abstract The purpose of this paper is to document Shell's experiences and learnings in the effort to better track and reduce Green House Gases (GHG) and improve Energy Efficiency in our downstream manufacturing and upstream production operations. The paper is based on Case Studies from various Operating Units in Shell upstream and downstream operations, as well as outlining further development plans. In Shell operations we seek to minimize GHG emissions by continuously monitoring, displaying and reporting associated Key Performance Indicators (KPI's) and quickly alerting operators of changes to trigger remedial intervention. Reduction of GHG emission is also achieved by improving cross validated and mass balanced tracking of our process streams. This ensures that manufacturing and production processes are operated efficiently and transparently. Continuous GHG monitoring also allows automatic compilation of emissions by source which can then be automatically reported as part of the normal daily reporting cycle. The resulting emissions figures and associated KPI's are then prominently displayed in the Daily Production Report. The daily emissions totals are also stored and trended to flag more subtle and/or gradual changes. In this way GHG emissions data and performance information are made available to operations staff and management to facilitate awareness and corrective actions when appropriate.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".