{"id":"W4401353423","doi":"10.2139/ssrn.4918204","title":"A Survey of Scientific Workflow Composition Interfaces","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Workflow; Composition (language); Computer science; Data science; Database","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":[0.01431066,0.002161776,0.001849284,0.007988614,0.001551273,0.01148957,0.005703078,0.003319709,0.007785835],"category_scores_gemma":[0.02901934,0.002351193,0.002286411,0.01229314,0.001052267,0.01360953,0.003794454,0.003697171,0.003971855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001855342,"about_ca_system_score_gemma":0.00397968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00229108,"about_ca_topic_score_gemma":0.001184288,"domain_scores_codex":[0.9868158,0.002333998,0.00236838,0.002050064,0.005381855,0.001049983],"domain_scores_gemma":[0.9825309,0.009774289,0.0009905923,0.003386545,0.002737206,0.0005804565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007742237,0.0004744901,0.004563175,0.005312104,0.0001627655,0.0002090768,0.001153361,0.003772574,0.006773391,0.08912949,0.0237582,0.8639172],"study_design_scores_gemma":[0.0001561542,0.0005758225,0.004890614,0.006129719,0.000442314,0.002066812,0.0008452097,0.04450854,0.03099984,0.0711243,0.8380316,0.0002290951],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.04917283,0.1213007,0.7657333,0.004329204,0.0009257322,0.0008783843,0.002276976,0.02244888,0.03293402],"genre_scores_gemma":[0.1300651,0.1322541,0.6896614,0.004003267,0.0009558094,0.0009873314,0.01149339,0.01018997,0.02038969],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01431066,"threshold_uncertainty_score":0.07568288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1002122391952169,"score_gpt":0.3795482453706516,"score_spread":0.2793360061754347,"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."}}