{"id":"W2959416902","doi":"10.1111/cgf.13715","title":"Segmentifier: Interactive Refinement of Clickstream Data","year":2019,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Computer science; Clickstream; Visual analytics; Glyph (data visualization); Process (computing); Data mining; Visualization; Path (computing); Set (abstract data type); Downstream (manufacturing); Human–computer interaction; Theoretical computer science; Programming language; The Internet; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.0002555934,0.0001336348,0.0002019594,0.0003528259,0.00004682078,0.000126396,0.002243151,0.00004524526,0.00003499851],"category_scores_gemma":[0.000008954888,0.0001239528,0.00006588809,0.0008340844,0.00004195044,0.0007421398,0.002344114,0.0001110015,0.00007843611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009705497,"about_ca_system_score_gemma":0.00004173043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001839736,"about_ca_topic_score_gemma":0.00001199593,"domain_scores_codex":[0.9985898,0.00004461952,0.0003385528,0.0004977706,0.0003007846,0.0002284757],"domain_scores_gemma":[0.9975076,0.00006525444,0.000196674,0.002030012,0.0001301064,0.00007034471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001075824,0.0004787363,0.0197235,0.00009119359,0.000215698,0.000007177589,0.0003292466,0.0000900134,0.0001766922,0.7581897,0.1791086,0.04157868],"study_design_scores_gemma":[0.0004894456,0.000165386,0.001307646,0.0000721043,0.00001278428,0.00000523213,0.00003577878,0.8874493,0.0006943629,0.002592124,0.106961,0.0002148489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003528723,0.00004115415,0.9932504,0.0005956209,0.001076578,0.000164115,0.00007835639,0.00009145503,0.001173589],"genre_scores_gemma":[0.9129167,0.0003228543,0.08001348,0.004492991,0.0001479244,0.000005931082,0.001243432,0.00003511321,0.0008215836],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9132369,"threshold_uncertainty_score":0.5054649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02750749594206824,"score_gpt":0.3002312375465728,"score_spread":0.2727237416045045,"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."}}