{"id":"W4253537795","doi":"10.12688/f1000research.6656.1","title":"NetMatchStar: an enhanced Cytoscape network querying app","year":2015,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"European Regional Development Fund","keywords":"Upload; Open peer review; Computer science; Graph; Plant biology; Computational biology; Information retrieval; Bioinformatics; World Wide Web; Biology; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001698022,0.0004394373,0.0004382725,0.00009235307,0.000192546,0.0002629957,0.00126587,0.0009672188,0.0001065658],"category_scores_gemma":[0.00007622758,0.0004383267,0.0001884986,0.0001561555,0.0001785038,0.000007985357,0.00282033,0.001111925,0.0001390836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007771557,"about_ca_system_score_gemma":0.0008464575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009575075,"about_ca_topic_score_gemma":0.0001958277,"domain_scores_codex":[0.996796,0.0002440207,0.0005457817,0.0007924343,0.0005670927,0.001054647],"domain_scores_gemma":[0.9972194,0.00002403452,0.0002010542,0.001610559,0.0004186304,0.000526345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001335245,0.0003629993,0.0009249274,0.0008392179,0.0008204465,0.00004063744,0.00121481,0.1531732,0.08095217,0.0008751118,0.6767487,0.08271252],"study_design_scores_gemma":[0.003850369,0.002321981,0.0008426613,0.0006841522,0.0001697165,0.0000656845,0.001065563,0.09332646,0.0435812,0.05079471,0.7988547,0.004442821],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8377761,0.01398927,0.06775111,0.0006484249,0.003541684,0.003408002,0.000366761,0.0003171968,0.07220148],"genre_scores_gemma":[0.9692039,0.0015917,0.01087198,0.0005806594,0.006550148,0.0002695318,0.004477215,0.0002065798,0.006248237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1314279,"threshold_uncertainty_score":0.9998069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03277091323734291,"score_gpt":0.3202493801142233,"score_spread":0.2874784668768803,"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."}}