Application of multivariate techniques in conservation planning frameworks
Bibliographic record
Abstract
Over the last few years systematic conservation planning has gained greater focus in freshwater ecology. As most conservation plans target multiple biodiversity features, this is inherently a multivariate problem. Apart from the use of multivariate modeling techniques to estimate taxa distributions, we present three applications of classification and ordination techniques in a conservation framework. Highly unique sites - thus of high conservation value - can be identified using the multivariate distance to a group centroid or their nearest neighbors. Systematic conservation planning algorithms, such as bootstrapped heuristics or simulated annealing, deliver multiple solutions to meet conservation targets. Cluster analysis and non-metric multidimensional scaling help to select complementary conservation plans out of a pool of possible solutions. When taxa or other biodiversity features cannot be extrapolated across the landscape, multivariate environmental similarity patterns are linked directly to species patterns to create environmental surrogates that can be used for spatial prioritisation. We will demonstrate how these three approaches can enhance applied conservation planning schemes using invertebrate data from Victoria, Australia and the Yukon Territory, Canada.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".