{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002708361,0.001409517,0.0007317576,0.002151645,0.0005018287,0.00217241,0.001627496,0.0009610403,0.01311441],"category_scores_gemma":[0.01021421,0.0006252346,0.0009884721,0.0009477811,0.0007884511,0.002763687,0.003363959,0.001453529,0.001679704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007217557,"about_ca_system_score_gemma":0.0007319521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005791226,"about_ca_topic_score_gemma":0.007425933,"domain_scores_codex":[0.9991309,0.0002260757,0.00006673308,0.0001869872,0.000315516,0.00007379617],"domain_scores_gemma":[0.9932768,0.004419168,0.0002901236,0.0009769651,0.0006979512,0.0003391249],"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":[0.004768509,0.0005849532,0.02158714,0.00183183,0.0002951736,0.00189518,0.01680813,0.04244703,0.1305832,0.04575226,0.09565863,0.6377881],"study_design_scores_gemma":[0.0003023802,0.0006064181,0.01028832,0.000535303,0.000128992,0.0006414808,0.002166326,0.6234157,0.1211449,0.04093295,0.199486,0.0003512934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04740278,0.000251705,0.8455648,0.000503789,0.0001047811,0.0004600186,0.005261787,0.09523748,0.00521278],"genre_scores_gemma":[0.2454633,0.0002683855,0.7292752,0.0003216715,0.00006261219,0.0005320698,0.00730653,0.01201124,0.004758925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01311441,"threshold_uncertainty_score":0.04387212,"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."}}