Bibliographic record
Abstract
Pillars of the Industry Contending with the oil and gas industry’s global and uncertain nature requires skill sets that span multiple professions, and a seamless integration between them. A passion to work with a diverse workforce attracts many of us to the oil and gas industry. When I arrived in Canada as a refugee, my first job was working at a holiday resort where the workforce included people from all over the world and this experience is something I cherish. If there is one thing that I can attribute to where I am today, it is the individuals I worked with in my first job. These were some of the best coaches and mentors that shaped my life. It is fair to say that my affiliation to the oil and gas industry was more by accident than design. As a professional in the oil and gas industry, a skill that is most dear to my heart is being part of an integrated team. As with life, one’s career is a journey and there is so much you can learn on the way. You need to keep an open mind throughout your career. The more complex the industry gets, the more the requirement for individuals who are good at integrating by forming strong long-lasting networks. The Current Industry Landscape We live in a world where oil production was predicted to reach its peak in the mid-2000s at just more than 80 million BOPD with demand expected to outstrip supply by 10 million BOPD in 2015. The reality is that world oil supply in the first quarter of 2015 was close to 96 million BOPD, with demand for oil at 94 million BOPD, according to data from the US Energy Information Administration. At the same time, oil prices have fallen more than 50% in the last year, and the world rotary rig count has dropped by close to 30%, according to recent data published by energyeconomist.com. In Australia, “nation-building” mega liquefied natural gas (LNG) projects have been constructed over the last decade and the country has significant LNG production capacity. A recent publication from the Reserve Bank of Australia forecasts LNG nameplate capacity growing from 20 million tonnes per annum (MTPA) in 2010 to 85 MPTA by 2018, at a time when downward oil prices are putting enormous pressure on oil and gas producers. According to Australia’s Department of Industry and Science, 2014–2015 LNG export value of about USD 18 billion is projected to increase at an annual growth rate of 21%, with export value expected to reach USD 47 billion by 2019–2020. By 2050, oil and gas is projected to supply close to two-thirds of Australia’s energy consumption. This growth is phenomenal. Clearly, just relying on what has happened in the past is not always the best yardstick for the future. The more we are apt to change, the higher our chances of survival.
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 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 | 0.000 |
| 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 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".