{"id":"W3031620263","doi":"10.1016/j.future.2020.05.029","title":"Topic-based crossing-workflow fragment discovery","year":2020,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"National Key Research and Development Program of China","keywords":"Workflow; Computer science; Fragment (logic); Relevance (law); Graph; Reuse; Information retrieval; Semantic Web; World Wide Web; Database; Theoretical computer science; Programming language; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002411666,0.001333947,0.001401011,0.007898995,0.002210664,0.003171759,0.003051617,0.001654002,0.009182881],"category_scores_gemma":[0.01127554,0.0005976887,0.002927013,0.008362961,0.0007258263,0.003462406,0.003695744,0.001639694,0.004320392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272848,"about_ca_system_score_gemma":0.005953414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02209607,"about_ca_topic_score_gemma":0.03121984,"domain_scores_codex":[0.99669,0.0003788859,0.0002390034,0.0009334878,0.001254019,0.0005044906],"domain_scores_gemma":[0.9936207,0.002166316,0.0003600273,0.00160526,0.00176151,0.000486206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002555305,0.0007505658,0.04927526,0.001785669,0.001019633,0.00270856,0.002502359,0.04018469,0.04129862,0.03429984,0.07077061,0.7528489],"study_design_scores_gemma":[0.0002649586,0.0003708113,0.02138487,0.000224486,0.0009242797,0.002755042,0.001843947,0.7789042,0.04152241,0.09003207,0.06154128,0.0002316862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08971471,0.00174698,0.8530858,0.0006571493,0.0003505231,0.0009456743,0.01761351,0.02509767,0.01078808],"genre_scores_gemma":[0.3516789,0.0007794374,0.5896982,0.0001925961,0.0001614986,0.0004185932,0.04720834,0.00150834,0.008354053],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02209607,"threshold_uncertainty_score":0.04393488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1229160400506566,"score_gpt":0.3256404200867224,"score_spread":0.2027243800360659,"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."}}