Bibliographic record
Abstract
The present climate of coal seam gas (CSG) production in east coast Australia illustrates the importance of consultation and engagement with the government and stakeholders. This extends particularly to agricultural and community groups, and the impact they have on government policy decisions and in some instances, knee-jerk reactions based on emotion rather than science. Farmers are (and have been) strong environmental managers who want to ensure that the protection of prime agricultural land is safeguarded for future generations—however, so do petroleum companies and working side-by-side for a successful outcome is achievable. For instance, AGL Energy has invested in the agricultural sector from vineyards to growing cattle, allowing the company to engage in the sector directly. On the ground early engagement strategies increasingly need to be implemented with agriculture, which reassures the government and provides a win-win outcome by diffusing anti-groups and community divisions by bringing opportunities for sustainable economic benefit. One of the critical questions is how can this be done successfully without it being seen by the government and community as corporate spin. Particular reference will be made to NSW and the relationship that AGL Energy has built with agriculture organisations, such as Dairy Connect NSW and community groups such as Advance Gloucester. This extended abstract will illustrate that the opportunities for growth for CSG, agriculture and the community are only limited by narrow views of what is achievable and what is drawn from real-life experiences from AGL Energy operations in NSW.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".