Bibliographic record
Abstract
FLOCK is designed to unravel genetic structure within a collection of genotypes, whether pure or admixed. It is assumed that pure, “source”, samples are not available. The program may be used to solve the “number (K) of populations” problem. Since FLOCK is a non-model algorithm, the populations may originate from sexual as well as clonal reproduction. When K is already known, it may be used to separate pure and admixed specimens into K groups. FLOCK is a non- Bayesian, non MCMC, method and therefore differs substantially from previous clustering algorithms and processing time is much shorter (20 sweeps). Its working principle is repeated re-allocation of all collected specimens (total sample) to k subsamples. The methods to map genetic admixture on a set of samples (K known) and to estimate the number of populations K were described respectively in: Duchesne P, Turgeon J (2009) FLOCK: a method for quick mapping of admixture without source samples. Molecular Ecology Resources 9: 1333-1344 Duchesne P, Turgeon J (2012) FLOCK Provides Reliable Solutions to the "Number of Populations" Problem Journal of Heredity 2012; doi: 10.1093/jhered/ess038 Three versions of FLOCK are available, one for microsatellite, one for AFLP and one for SNP markers. The basic algorithm, input and output formats are the same in the three versions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.313 | 0.184 |
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".