<scp>geoorigins</scp>: A new method and<scp>r</scp>package for trait mapping and geographic provenancing of specimens without categorical constraints
Bibliographic record
Abstract
Abstract Biologists often seek to geographically provenance organisms using their traits. This is typically achieved by defining spatial groups using distinct patterns of trait variation. Here, we present a new spatial provenancing and trait boundary identification methodology, based on correlations between geographic and trait distances that require no a priori group assumptions. We apply this to three datasets where spatial provenance is sought: morphological rat and vole dentition data (human commensal translocation datasets); and birdsong data (cultural transmission dataset). We also present the results of cross‐validation testing. Spatial provenancing is possible with differing degrees of accuracy for each dataset, with birdsong providing the most accurate geographic origin (identifying an average spatial region of 0.22 km2as the area of origin with 99.9% confidence). Our method has a wide range of potential applications to diverse data types—including phenotypic, genetic and cultural—to identify trait boundaries and spatially provenance the origin of unknown or translocated specimens where trait differences are geographically structured and correlated with spatial separation.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.071 | 0.037 |
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".