World trends and innovations in production asset management—case studies from Australia and North America*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Australia is transitioning to become an energy superpower—the $43 billion Gorgon LNG project and the other comparably-sized projects lining up behind the Gorgon project confirm this. There are predictions that around $80 billion of CAPEX on LNG projects will be approved for expenditure for the 2010 financial year with much more to be invested in the following years. And, we are on the cusp of further coal seam gas developments in Queensland, which could see annual production rise from 130 to more than 3,000 petajoules per annum once the infrastructure is in place. What are the skills needed to realise the true potential of these investments? An appropriate asset management plan is key. Asset management is more than the provision of maintenance services—it is about developing a systematic approach to managing an asset during its life and achieving the outputs required by the owner of the asset. Program and project management of brownfield capital works, maintenance services and infrastructure projects are also essential technical capabilities to help meet the demand of the burgeoning LNG and coal seam gas industries. These skills will determine who can deliver on schedule, or ahead of it. The other key capability will be mobilising, managing and retaining people. There is speculation that the Queensland coal seam gas industry alone will generate approximately 12,000 jobs. The industry needs to be prepared to be innovative in engaging, training and upskilling people. As the only true global resources and industrial provider in Australia, Transfield Services will share its key learnings on effectively managing assets, projects and people from its work with clients including Canada’s largest energy company, Suncor Energy.
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.
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 | 0.000 | 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 | 0.000 |
| 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 it