Development by Design in Western Australia: Overcoming Offset Obstacles
Bibliographic record
Abstract
Biodiversity offsets can be an important tool for maintaining or enhancing environmental values in situations where development is sought despite negative environmental impacts. There are now approximately 45 compensatory mitigation programs for biodiversity impacts worldwide, with another 27 programs in development. While offsets have great potential as a conservation tool, their establishment requires overcoming a number of conceptual and methodological hurdles. In Australia, new policy changes at the national and state (i.e., Western Australia) level require that offsets follow a set of general principles: (1) Environmental offsets may not be appropriate for all projects and will only be considered after avoidance and mitigation options have been pursued; (2) Environmental offsets will be based on sound environmental information and knowledge; (3) Establishing goals for offsets requires an estimate of expected direct and indirect impacts; (4) Environmental offsets will be focused on longer term strategic outcomes; (5) Environmental offsets will be cost-effective, as well as relevant and proportionate to the significance of the environmental value being impacted. Here we focus on the challenges of determining and implementing offsets using a real world example from a voluntary offset process undertaken for Barrick Gold’s Kanowna Belle mine site in Western Australia to highlight those challenges and potential solutions.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".