Safety leadership and collaboration in the Queensland natural gas exploration and production industry
Bibliographic record
Abstract
The industry in Queensland operates within a common geographical area and uses similar technologies with common hazards and risks. In terms of safety, companies must be seen as one industry and not separate entities. As a result, collaboration on safety is a natural outcome, and in 2014 this led to the creation of the Queensland Natural Gas Exploration & Production Industry Safety Forum (known as Safer Together), an inclusive member-led organisation of a range of operating and contract partner companies. Initial emphasis was on the set-up/organisation and getting early engagement. With more than 80 companies signed up as members in the first 12 months, Safer Together made a strong start. The emphasis has now switched to delivery, and with all member companies feeling the strain of the industry downturn, working together has never been so crucial to ensure that safety is never compromised. This extended abstract presents a case study of what Safer Together is learning about the fundamental prerequisites required to ensure long-term sustainability and the success of the forum. Challenges discussed include:maintaining and increasing membership in tough times;ensuring senior leaders continue to be actively engaged, regardless of other business pressures;ensuring simple solutions don’t become too difficult to implement when rolled out to many different companies;avoiding initiative overload; and,demonstrating tangible value to member companies. This is not an easy journey, and more challenges lie ahead. But the enormous safety benefits make it the right thing to do as an industry.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".