{"id":"W255407626","doi":"10.1007/978-3-319-07221-0_21","title":"The Usefulness of Log Based Clustering in a Complex Simulation Environment","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Cluster analysis; Exploratory data analysis; Context (archaeology); Data mining; Point (geometry); Exploratory analysis; Machine learning; Artificial intelligence; Action (physics); Layer (electronics); Exploratory research; Data science","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":[0.002350155,0.0005706215,0.000613922,0.001216328,0.0008234649,0.001563812,0.001230372,0.001239954,0.002054885],"category_scores_gemma":[0.02262853,0.0004210016,0.0002836009,0.001114381,0.0007269609,0.003113879,0.001201383,0.0007595106,0.0004370121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008091512,"about_ca_system_score_gemma":0.0006481622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004894176,"about_ca_topic_score_gemma":0.004082734,"domain_scores_codex":[0.9984688,0.0009296772,0.00005586221,0.0001703603,0.0003078091,0.00006745901],"domain_scores_gemma":[0.9738818,0.02102566,0.0008053334,0.0021998,0.001548722,0.0005386788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002902427,0.0006211231,0.01482466,0.0001959168,0.00008111071,0.0001642853,0.0005949304,0.6959942,0.009196607,0.009525236,0.002419157,0.2634802],"study_design_scores_gemma":[0.00001907029,0.0001674495,0.001847475,0.000008224455,0.0000190632,0.00004544729,0.00008536936,0.9916896,0.002122289,0.003700534,0.0002789499,0.00001664638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5116547,0.0004235651,0.4743772,0.0004923341,0.00009692623,0.0001273658,0.0001953828,0.003919864,0.008712698],"genre_scores_gemma":[0.9499907,0.00009395659,0.04856826,0.00002930593,0.0000245084,0.00002774984,0.000109318,0.0002082316,0.0009480374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004894176,"threshold_uncertainty_score":0.01242894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04209161734720487,"score_gpt":0.2484196301307797,"score_spread":0.2063280127835748,"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."}}