{"id":"W2023357311","doi":"10.1109/tvcg.2013.214","title":"Variant View: Visualizing Sequence Variants in their Gene Context","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Canada's Michael Smith Genome Sciences Centre","keywords":"Computer science; Visualization; Workflow; Context (archaeology); Sequence (biology); Task (project management); Data visualization; Encoding (memory); Information retrieval; Data science; Human–computer interaction; Data mining; Artificial intelligence; Database; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003818252,0.0003035732,0.0003076296,0.00062498,0.0002760499,0.0005772515,0.0005172502,0.0001424871,0.00005327197],"category_scores_gemma":[0.00000600395,0.0002856861,0.00008326882,0.001518135,0.00009287607,0.001150573,0.00001555202,0.0002135388,0.00005184277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004068986,"about_ca_system_score_gemma":0.00006989535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001338271,"about_ca_topic_score_gemma":0.00005814833,"domain_scores_codex":[0.9978587,0.0002879514,0.0005554451,0.0006276487,0.0003185838,0.0003516666],"domain_scores_gemma":[0.9988164,0.0001462761,0.000139305,0.0004918674,0.0002117682,0.0001944327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006351321,0.0005271125,0.0001377147,0.00004652449,0.00005886683,0.00002078923,0.00209582,0.0006723896,0.0002510333,0.9395597,0.0004589274,0.05616479],"study_design_scores_gemma":[0.0006194853,0.0001121718,0.0005115502,0.00008508112,0.000009038974,0.00004497044,0.00007136732,0.9933936,0.001475586,0.00202563,0.001301771,0.0003497216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005209417,0.00006386263,0.993373,0.0001538424,0.0005446505,0.0003445046,0.00002217587,0.0002415001,0.00004702015],"genre_scores_gemma":[0.9915636,0.0007551739,0.002267511,0.005199821,0.00004428745,0.00004728228,0.00002398203,0.00002805442,0.00007028081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9927213,"threshold_uncertainty_score":0.9999595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03505998007357396,"score_gpt":0.2872464593803011,"score_spread":0.2521864793067272,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}