FLOCK: a method for quick mapping of admixture without source samples
Bibliographic record
Abstract
Identifying and estimating individual and/or population admixture is a very common objective in evolution and conservation biology. There are many situations where samples from one or many of the putatively hybridizing entities are not available or easily identified. Here we describe FLOCK, a new method especially designed to provide spatial and/or temporal admixture maps in the absence of one or several source samples. FLOCK is a non-Bayesian method and therefore differs substantially from previous clustering algorithms. Its working principle is repeated re-allocation of all collected specimens (total sample) to the k subsamples, each re-allocation being more effective than the previous one in attracting genetically similar individuals. This snowball effect, more formally referred to as a positive feedback mechanism, makes FLOCK an efficient and quick sorting process. The usage of FLOCK is illustrated with two empirical situations which have been thoroughly analysed previously with other approaches. A number of simulations were run to better assess the power of the FLOCK algorithm. Performance comparisons were made between the FLOCK and Structure algorithms. When non-negligible numbers of pure genotypes were present, the two performed equally well. However, FLOCK proved significantly more powerful in the absence of pure genotypes. Moreover, FLOCK showed more potential for fast processing. Run times were shown to increase linearly with size of total sample and with size of k, the number of reference samples from which admixture mapping is performed.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".