{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001518561,0.0001906784,0.0002858031,0.0001281955,0.0005155123,0.007595805,0.0008926779,0.00007002286,0.00006976391],"category_scores_gemma":[0.0000971953,0.0001404129,0.0001301981,0.0007406292,0.0000448969,0.0004487066,0.0002769796,0.0001105709,0.0003557162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004415901,"about_ca_system_score_gemma":0.000099112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000168111,"about_ca_topic_score_gemma":0.00001106187,"domain_scores_codex":[0.9962147,0.000304553,0.0007527457,0.001001772,0.001474807,0.0002514645],"domain_scores_gemma":[0.998221,0.0001393773,0.0002529713,0.0009790182,0.000227994,0.0001796096],"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":[0.000005957722,0.00002338464,0.0004873964,0.00001113364,0.00001083852,0.000008898486,0.0004234296,0.1106934,0.00009505122,0.001415257,0.8541961,0.03262912],"study_design_scores_gemma":[0.0001831888,0.00003808192,0.0003841454,0.000009211007,0.000003537719,0.000001221602,0.0000648613,0.483567,0.00006877055,0.000006576341,0.515566,0.0001073912],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01456937,0.0003249403,0.8815867,0.007802199,0.0950342,0.0002701432,0.00002530041,0.0001242354,0.0002628885],"genre_scores_gemma":[0.6261542,0.000002531365,0.04436066,0.01147121,0.3131778,0.00004308737,0.0003121709,0.00003417418,0.004444122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.837226,"threshold_uncertainty_score":0.9934344,"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."}}