A conservation auction for landscape linkage in the southern Desert Uplands, Queensland
Bibliographic record
Abstract
Conservation auctions are a type of market-based instrument (MBI) that can achieve a more cost-efficient allocation of public funds than approaches such as devolved grants. In this paper, the conduct of a multiple round conservation auction to improve biodiversity management in a rangelands area is outlined. The auction was designed to develop a wildlife corridor across the southern Desert Uplands bioregion in Queensland and to improve management of rangelands areas. The conservation auction incorporated two important new design features. First, there was a need to promote landholder cooperation so that proposed areas for better land management were aligned and connected across the region. The second innovative design feature was to hold multiple bidding (three) rounds, which differs from the standard application of a single bidding round. The auction outcomes resulted in conservation contracts covering 85 000 ha of remnant vegetation awarded at an average cost of $2* per hectare per annum. Although complete landscape connectivity across the Desert Uplands was not achieved, over 70% of the successful bids, accounting for over 62 000 ha (77% of the total bid area), were part of a group that formed a distinct corridor or landscape linkage with only single or part-property gaps. The results also indicate that multiple bidding rounds improved auction efficiency (for the government), although there was little improvement in connectivity. Sixty-six percent more environmental benefit units could be purchased for the given budget of $350 000 between rounds one and three.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".