FLOCK Provides Reliable Solutions to the “Number of Populations” Problem
Bibliographic record
Abstract
Identifying groups of individuals forming coherent genetic clusters is relevant to many fields of biology. This paper addresses the K-partition problem: given a collection of genotypes, partition those genotypes into K groups, each group being a sample of the K source populations that are represented in the collection of genotypes. This problem involves allocating genotypes to genetic groups while building those groups at the same time without the use of any other a priori information. FLOCK is a non-Markov chain Monte Carlo (MCMC) algorithm that uses an iterative method to partition a collection of genotypes into k groups. Rules to estimate K are formulated and their validity firmly established by running simulations under several migration rates, migration regimes, number of loci, and values of K. FLOCK tended to build clusters largely consistent with the source samples. The performance of FLOCK was also compared with that of STRUCTURE and BAPS. FLOCK provided more accurate allocations to clusters and more reliable estimates of K; it also ran much faster than STRUCTURE. FLOCK is based on an entirely novel approach and provides a true alternative to the existing, MCMC based, algorithms. FLOCK v.2.0 for microsatellites or for AFLP markers can be downloaded from http://www.bio.ulaval.ca/no_cache/departement/professeurs/fiche_des_professeurs/professeur/11/13/.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".