Accurate delineation of biogeographical regions depends on the use of an appropriate distance measure
Bibliographic record
Abstract
Abstract The use of analytical techniques to delineate biogeographical regions is becoming increasingly popular. One recent example, Heikinheimo et al . ( Journal of Biogeography , 2007, 34 , 1053–1064 ), applied the k ‐means clustering algorithm to define the biogeography of the European land mammal fauna. However, they used the Euclidean distance measure to cluster grid cells described by species‐occurrence data, which is inappropriate. The Euclidian distance yields misleading results when applied to species‐occurrence data because of the double‐zero problem and the species‐abundance paradox. We repeat their analysis using the Hellinger distance, a measure appropriate for species‐occurrence data and which has been shown to outperform other such measures. Our results differ substantially from those presented by Heikinheimo et al. We argue that the rigorous application of appropriate statistical techniques is of crucial concern within conservation biogeography.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".