Lessons learned from developing offsets in the Brigalow Belt of Queensland
Bibliographic record
Abstract
Origin Energy—on behalf of the Australia Pacific LNG Project, the upstream tenures of which occupy some 570,000 ha—has established an offsets project in the Brigalow Belt of Queensland. This belt of Acacia woodland runs between the tropical rainforest of the coast and the semi-arid interior of Queensland and is one of 15 bio-regions in Queensland. Its reduction to less than 8% of its distribution makes it a significant part of Australia’s natural environment. The offsets project has the aim of re-establishing areas of Brigalow and associated vegetation communities and fauna habitats for impacts on matters of federal or state significance. This should result in a long-term reduction in environmental impacts. Given the long-term decline in Queensland’s biodiversity, such work is vital and its associated challenges—such as gaining long-term access to and protection of appropriate land, comparatively new and evolving regulatory requirements and, at times, working on the edge of scientific knowledge—requires persistence and innovation. This extended abstract presents valuable lessons learned to help inform future offsetting projects.
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".