Distance‐based population classification software using mean‐field annealing
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Bibliographic record
Abstract
We describe a distance-based clustering method using a proximity matrix of genetic distances to partition populations into genetically similar groupings. The optimization heuristic mean-field annealing (MFA) was used to find locally optimal solutions where exhaustive search was not possible. To illustrate this method, we analysed both simulated and real data sets. Simulated data indicated that MFA successfully differentiated population groups, even with small F(ST) values, as long as there was separation of within and between group distances. Reanalysis of microsatellite data from various human populations using mean-fields found similar ethnic groups corresponding to major geographic regions reported by Rosenberg et al. (2002) who used the model-based computer program Structure. However, with MFA, the Kalash population was found to group with other Central/South Asian populations instead of being the only member of its own genetic cluster. Europe/Middle East populations formed a separate group from Central/South Asian populations instead of being a single group in the Structure analysis. The MFA analysis determined the greatest genetic distances (largest mean intracluster distance) occurred in native American populations, identifying three groups instead of only one found with Structure. For conservation purposes, it is not only important to identify genetically similar groupings but also to determine the relative level of genetic differentiation captured within these groups. To illustrate this, we compare two separate MFA analyses of Chinook salmon (Oncorhynchus tshawytscha) populations from British Columbia, Canada. The software called PORGS-MFA used in this article can be downloaded from http://www.pac.dfo-mpo.gc.ca/science/facilities-installations/pbs-sbp/mgl-lgm/apps/porgs/index-eng.htm.
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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 | 0.000 |
| 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) | 0.000 | 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 it