{"id":"W2545286693","doi":"10.1101/082776","title":"Towards an ontology-based recommender system for relevant bioinformatics workflows","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Montréal","keywords":"Workflow; Computer science; Ontology; Reuse; Data science; Identification (biology); Recommender system; Domain (mathematical analysis); Information retrieval; Data mining; Database","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01215745,0.0007091337,0.00105093,0.001036292,0.0005049651,0.001840372,0.00369329,0.000584982,0.00007572291],"category_scores_gemma":[0.003071949,0.0005316475,0.0003966729,0.00095549,0.0001949946,0.0004817757,0.001524928,0.0004712522,0.0003916478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005659843,"about_ca_system_score_gemma":0.001029667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002612348,"about_ca_topic_score_gemma":0.000008521644,"domain_scores_codex":[0.9925889,0.0004381659,0.002012423,0.002233893,0.001725621,0.001000995],"domain_scores_gemma":[0.9894998,0.0009360724,0.001478821,0.005982755,0.001563452,0.0005391086],"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":[0.002111551,0.003835487,0.01809034,0.01039308,0.002697054,0.000467677,0.0006009066,0.009476848,0.04491474,0.1069985,0.7843375,0.01607635],"study_design_scores_gemma":[0.004753331,0.0005140898,0.0157783,0.00367468,0.0005127875,1.05872e-7,0.0002202334,0.4390364,0.02241512,0.000649707,0.5080029,0.004442337],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05153813,0.0002821473,0.9200009,0.002819381,0.01876651,0.002600621,0.002255592,0.001423198,0.0003135337],"genre_scores_gemma":[0.8475551,0.000009877899,0.1505737,0.000634476,0.0007300859,0.0003435189,0.00000286302,0.0001048667,0.00004553205],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7960169,"threshold_uncertainty_score":0.9997135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08257203695695378,"score_gpt":0.3181687545670103,"score_spread":0.2355967176100565,"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."}}