{"id":"W7011367836","doi":"","title":"Longitudinal Data Analysis: Understanding Visit Irregularities","year":2024,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Data collection; Longitudinal data; Set (abstract data type); Identification (biology); Data set","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":[0.09626828,0.00122807,0.002571324,0.00446827,0.001821897,0.005855715,0.003398752,0.003252819,0.004573704],"category_scores_gemma":[0.2737352,0.001478091,0.003857532,0.007430204,0.004517216,0.008686298,0.004130295,0.006266644,0.0009506923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002705462,"about_ca_system_score_gemma":0.003637296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007213281,"about_ca_topic_score_gemma":0.005429835,"domain_scores_codex":[0.9437411,0.04257535,0.0031186,0.007002994,0.002846036,0.0007159456],"domain_scores_gemma":[0.642085,0.2962177,0.01911399,0.03449008,0.006877485,0.001215809],"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":[0.0005529884,0.0002366653,0.1932281,0.002370801,0.00321138,0.000803775,0.006351218,0.06815846,0.001539492,0.3138463,0.02598906,0.3837117],"study_design_scores_gemma":[0.0001034981,0.0004650955,0.03986636,0.001191077,0.00048813,0.0008912035,0.002186315,0.3876368,0.0008440579,0.5263885,0.03974696,0.0001920481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01105929,0.002207478,0.9795014,0.00397526,0.0002069825,0.0002274489,0.001077039,0.0005733332,0.001171727],"genre_scores_gemma":[0.3407781,0.005270833,0.6381819,0.002641346,0.001297627,0.003051582,0.004594363,0.0007293427,0.003455029],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09626828,"threshold_uncertainty_score":0.5091214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06601562018556069,"score_gpt":0.2917143083204691,"score_spread":0.2256986881349084,"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."}}