Where to survey? Spatial biodiversity survey gap analysis: a multicriteria approach
Bibliographic record
Abstract
The aim of this study was to quantify the relative effort for biodiversity surveys across the public forest estate in the south-west of Western Australia. We collated information on historical surveys into a metadatabase and recorded locations where surveys had been conducted in a spatial geodatabase. We then used multicriteria modelling to rank land conservation units on the basis of relative survey effort. The results indicated that the western, particularly the south-western, parts of the study area were relatively well surveyed while eastern parts were relatively poorly surveyed. This is likely to reflect greater habitat loss and fragmentation of vegetation on the eastern margins of the forest estate where it adjoins the extensively cleared Western Australian wheatbelt. There was also an emphasis on monitoring biodiversity in forest habitats closer to the main population centres of the south-west. The results of this analysis provide a basis for assessing future survey needs for the region, which should also consider: patterns of distribution in species richness; the extent, connectivity and conservation status of native vegetation; and the relative risks posed to biodiversity by infrastructure and industrial land uses. We discuss the potential limitations of the multicriteria modelling approach in the context of our study.
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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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".