Conservation of native woodland by farmers in Moree Plains Shire, New South Wales
Bibliographic record
Abstract
Summary The New South Wales Government introduced the Native Vegetation Conservation Act 1998 to protect the native woodland and native grassland of the state. The amounts of native vegetation already conserved prior to the Act, the costs of continued conservation under the Act, and the farmers' wish to conserve or clear, are essential information to assist policy development in this area. To provide this kind of information, fifty-one farmers were interviewed in an important cropping region of the state, Moree Plains Shire. On average, 21.0% of the area of each farm in the sample was native woodland, and another 19.9% was native grassland. Over a quarter of the farms had at least 25% of their land in native woodland, and well over one-half had more than 10% in native woodland. The continued protection of this native vegetation under the Act imposes small costs on some landholders and high costs on others. Almost one-quarter of the farmers are losing only 5% or less of their potential income, but another quarter are losing at least one-half of their potential income. The farmers consider offsets to be an effective way for the state to promote conservation and compensate for some of their losses, and their wide range of suggestions for different kinds of offset is documented. The landholders who wish to clear more woodland are the poorer farmers who have the highest proportions of native woodland and grassland on their properties. The results are discussed in the context of current changes in the legislation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".