Structuration of phenotypes and genotypes through galois lattices and implications
Bibliographic record
Abstract
The Galois Lattice of a binary relation formalizes it as a concept system, dually ordered in "extension"/"intension." All implications between conjunctions of properties holding in it are summarized by a canonical basis--all basis having the same cardinality. We report how these tools structure phenotypes/genotypes in behavior genetics. The first study on phenotypes of laterality has a unique set of features and two sets of instances (left-/right-handers) for which the corresponding sets of rules are compared, while the second study on partial trisomy 21 uses a unique instance set (patients) to explore the matching between two sets of features: phenotypes and genetic causes. Hence, both situations comprise two binary data sets that are paired through either a column or a row matching, which raises specific questions. If the data are small, as compared with databases in bioinformatics, this illustrates how these abstract tools can unfold better interpretations.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".