Automated Greenhouse Gas Data Collection, Visualisation and Reporting
Bibliographic record
Abstract
Abstract In 2008/2009 Apache Energy Limited (AEL) triggered the Australian National Greenhouse and Energy Reporting (NGER) Act threshold, requiring AEL to compile and report energy and emissions datasets for the financial year. This was in addition to the Apache Corporate calendar year emissions reporting requirements. Initially this was managed with EXCEL spread sheets, and the process was cumbersome and time consuming. Today I’m going to present the way in which the use of simple technology has enabled Apache to streamline its GHG reporting. In 2010 AEL developed real time, online flare/emissions pages, using the visualisation software BabelFish. The real time data is collected from the offshore Distributed Control System (DCS) using an automatic data collector, and it is stored in a PI (Process Information) Historian database. These visualisation pages "talk" to the PI Historian database and give real time data for the volume of CO2 equivalent emitted from all sources, for all the facilities operated or controlled by AEL. All data, trends, and year-end forecasts are available for anyone with access to see ‘real time’. The data stored in the PI Historian reduces the burden in producing emissions reports by capturing month end emissions data within the Production Reporting System (PRS), which can then be used to generate the NGERS report and the Apache corporate emissions report in a matter of minutes as opposed to months. With the arrival of the Clean Energy Legislation, and the tax on carbon emissions, it is imperative that companies have a good understanding of their emissions. Streamlining the reporting process and providing real time emissions data to management will help underpin this understanding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".