Automatic detection of n-degree family members
Bibliographic record
Abstract
Abstract Summary Research in the familial aggregation of diseases and traits utilise information on probands, and their relevant health information enriched with similar information for their family members of interest. The genealogy is typically generated from trio information in registers and biobanks. However it can be tedious and error prone to identify family members other than first-degree relatives. Here, we present a graph-based approach to effectively identify family members of arbitrary degree of relatedness, as well as the means to attach any desired information to each individual for downstream analysis and a function to efficiently calculate a kinship matrix for the identified family members and convert identified family members from a graph back into trio information. Availability and Implementation The R package where these new functionalities are implemented is available on GitHub ( https://github.com/EmilMiP/LTFHPlus ) and on CRAN ( https://cran.r-project.org/web/packages/LTFHPlus/index.html ).
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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 | 0.001 | 0.001 |
| 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".