A system for exact and approximate genetic linkage analysis of SNP data in large pedigrees
Bibliographic record
Abstract
Vol. 29, No. 2, 2013, pp. 197–205 doi:10.1093/bioinformatics/bts658 The publishers regret that the author affiliations for this paper should appear as follows: Mark Silberstein1,2, Omer Weissbrod1,*, Lars Otten3, Anna Tzemach1, Andrei Anisenia1,4, Oren Shtark1, Dvir Tuberg1, Eddie Galfrin1, Irena Gannon1, Adel Shalata5,6,7, Zvi U. Borochowitz5,8, Rina Dechter3, Elizabeth Thompson9 and Dan Geiger1 1Department of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel, 2Department of Computer Science, University of Texas at Austin, Austin, TX, USA, 3Donald Bren School of Information and Computer Sciences, UC Irvine, CA, USA, 4Department of Computer Science, University of Ottawa, Ottawa, Canada, 5The Simon Winter Institute for Human Genetics, Bnai-Zion Medical Center, Haifa, Israel, 6Research and Development Center, The Galilee Society, Shefa-Amr, Israel, 7Holy Family Hospital, Nazareth, Israel, 8The Rappaport Faculty of Medicine and Research Institute, Technion-Israel Institute of Technology, Haifa, Israel and 9Department of Statistics, University of Washington, Seattle, WA, USA
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 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".