{"id":"W4313315749","doi":"10.1371/journal.pone.0278424","title":"Different structural variant prediction tools yield considerably different results in Caenorhabditis elegans","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Caenorhabditis elegans; Genome; Computer science; Computational biology; Ground truth; Human genome; Structural variation; Biology; Data mining; Genetics; Artificial intelligence; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003058818,0.001143961,0.0006715837,0.001761419,0.0007368143,0.0008818212,0.0006865741,0.0009394071,0.0007448277],"category_scores_gemma":[0.007747358,0.0003014107,0.001087955,0.001114221,0.0004057378,0.0007474729,0.0006529419,0.0008148227,0.0004896067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005427778,"about_ca_system_score_gemma":0.0006552649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00985912,"about_ca_topic_score_gemma":0.02008616,"domain_scores_codex":[0.99756,0.0004707582,0.0002465469,0.0009417566,0.0005665835,0.0002144606],"domain_scores_gemma":[0.9961462,0.002441094,0.0001761767,0.0004344912,0.0006413188,0.0001606508],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.004991644,0.0006930043,0.1625783,0.002129786,0.0028332,0.001237207,0.0005284958,0.3336687,0.2188255,0.002131292,0.01452618,0.2558567],"study_design_scores_gemma":[0.0001687872,0.001576661,0.124614,0.0002611102,0.0006700291,0.0008990319,0.000582486,0.6891379,0.1673035,0.003714547,0.01076681,0.000305192],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9571593,0.002041867,0.0273004,0.0001858365,0.0001352217,0.00005983542,0.006664975,0.004446008,0.002006622],"genre_scores_gemma":[0.9089693,0.0006480789,0.06927275,0.0001374322,0.00001450209,0.00006241352,0.01924338,0.0005460711,0.001105989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9969412,"threshold_uncertainty_score":0.01960343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03702795164456584,"score_gpt":0.2079596368635657,"score_spread":0.1709316852189999,"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."}}