Investigating biogeographical patterns using point‐based cartograms
Bibliographic record
Abstract
Abstract Aim Visualization is an important tool in the investigation of phylogenetic distributions of species. Several tools have been developed that allow a researcher to overlay species distributions and phylogenetic trees on a map. However, when samples span a large range but also have high density in some regions, it can be difficult to appreciate both the global and the local context of biodiversity in a single view. Innovation We have developed an algorithm for cartogram construction that extends the Gastner–Newman approach to point‐based rather than region‐based data. Cartogram construction is controlled by parameters that impact the magnitude and extent of the distortion. We also introduce the geographically coupled phylogenetic distance (GCPD), a quantitative measure that combines phylogenetic diversity with geographical distance, as a criterion for distorting a map. Main conclusions We used our cartogram approach to develop enhanced geographical visualizations of datasets, including an outbreak of Vibrio cholerae in Haiti and the distribution of the salamander Aneides lugubris in California, U.S.A. In both examples, our cartogram approach allowed the concurrent visualization of local distributional patterns while preserving the broader context of the survey. Our implementation in the GenGIS software package allows joint visualization of cartogram, phylogenetic and other types of data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".