Invited: Fast and theoretically strong algorithms for kinship discovery
Bibliographic record
Abstract
In kinship inference, genealogical relationships among organisms, typically in naturally-occurring populations, based on genetic marker information are identified. This task is crucial to conservation of endangered species and to understand the diversity of populations. Some of the simplest problems in this domain are sib group and half-sibgroup discover. Natural objectives in this domain are statistical ones (such as maximum likelihood) and combinatorial one (such as parsimony). Unfortunately, even with error-free data, the simplest combinatorial objective, minimizing the number of matings, is NP-hard to approximate; the statistical objectives are even more challenging. Here, a simple combinatorial approach for the problem is shown. By enumerating triplets of population members that could be siblings and that could not be siblings, putative sibgroups are greedily constructed, merging them until no further mergings can occur. The simple algorithm performs comparably to or better than integer programming methods for the problem, in a tiny fraction of the runtime. Moreover, with high probability, these methods find the correct sibgroups, under a straightforward and standard probabilistic model of inheritance and mating. Hence, the NP-hardness of the original problem is ameliorated in "typical" instances of the problem. This phenomenon is common to a large variety of bioinformatics problems, so a discussion of how to respond to this observation is presented.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.021 |
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".