{"id":"W2221351895","doi":"","title":"It's about time: purpose, methods, and challenges of temporal analyses of multiple data streams","year":2010,"lang":"en","type":"article","venue":"International Conference of Learning Sciences","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Simon Fraser University","funders":"","keywords":"Computer science; Data stream mining; Data science; Coding (social sciences); Perspective (graphical); STREAMS; Data mining; Set (abstract data type); Data set; Data collection; Face (sociological concept); Machine learning; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001670405,0.000104012,0.0002585001,0.0002482894,0.00007022103,0.00008686146,0.002185102,0.00005219967,0.00005196121],"category_scores_gemma":[0.001709701,0.00008510544,0.00004136587,0.0002628817,0.0006406992,0.0006038341,0.0005795279,0.000237519,0.000002053484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002773242,"about_ca_system_score_gemma":0.0001881708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002607598,"about_ca_topic_score_gemma":0.00007819079,"domain_scores_codex":[0.9984514,0.0001452673,0.0003795425,0.0003922414,0.0005014819,0.0001300676],"domain_scores_gemma":[0.9980653,0.0005611791,0.0005280198,0.0003621735,0.0004339433,0.00004937057],"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":[0.0000173247,0.0002638494,0.1362582,0.0001163833,0.0001652491,0.000003629237,0.002209828,0.002267474,0.2000225,0.1028157,0.00006410357,0.5557958],"study_design_scores_gemma":[0.0002016417,0.0002233989,0.009090959,0.0001412507,0.00001627282,0.000008087662,0.0007234254,0.9700805,0.01480638,0.002620578,0.001943402,0.0001441297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8596414,0.00149985,0.1009592,0.01833578,0.0008191665,0.0001779623,0.00006309913,0.0001123407,0.01839124],"genre_scores_gemma":[0.8287041,0.0002531137,0.1708519,0.000007341909,0.00002646736,5.517078e-7,0.00000761413,0.000002566899,0.0001463445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.967813,"threshold_uncertainty_score":0.4060499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1920179239580016,"score_gpt":0.4452308270013598,"score_spread":0.2532129030433582,"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."}}