{"id":"W4224984058","doi":"10.1145/3491102.3517493","title":"Reflective Spring Cleaning: Using Personal Informatics to Support Infrequent Notification Personalization","year":2022,"lang":"en","type":"article","venue":"CHI Conference on Human Factors in Computing Systems","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Personalization; Computer science; World Wide Web; Internet privacy; Human–computer interaction","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002741427,0.0002572196,0.0003678831,0.001186887,0.001003209,0.0008215846,0.0008290523,0.00005812492,0.0002936871],"category_scores_gemma":[0.0002100104,0.0002460431,0.00009767443,0.001069546,0.00004313447,0.0005337118,0.0003667223,0.0003804707,0.00007259704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009021914,"about_ca_system_score_gemma":0.0001378969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002214502,"about_ca_topic_score_gemma":0.00003346427,"domain_scores_codex":[0.9956486,0.0003055472,0.001324237,0.0003990458,0.001942759,0.0003798051],"domain_scores_gemma":[0.9982332,0.0001878191,0.0007702354,0.0003698982,0.0003085097,0.0001303191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006902399,0.0002482121,0.4809905,0.0001002967,0.00004217721,0.00001003418,0.2051293,0.2194507,0.002404378,0.08704583,0.001503349,0.003006184],"study_design_scores_gemma":[0.0006013049,0.0004168081,0.1254424,0.0001660181,0.00001681193,0.000007751789,0.08506638,0.7850056,0.0001099344,0.0002824672,0.002241551,0.0006429732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774421,0.00000333985,0.0169101,0.00004816354,0.0008595035,0.0005933192,0.00002287825,0.00008624083,0.004034361],"genre_scores_gemma":[0.9990168,2.658582e-7,0.0002727906,0.0001979683,0.00007326438,0.0000201403,0.00005196896,0.00001684838,0.0003499425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.565555,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5417988898023494,"score_gpt":0.4794038680892432,"score_spread":0.06239502171310618,"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."}}