{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00740266,0.001461357,0.000740914,0.001584966,0.001486051,0.003992871,0.003407206,0.001330829,0.005326753],"category_scores_gemma":[0.03259413,0.001112359,0.0008653355,0.001030891,0.001043545,0.004814397,0.005620903,0.001923903,0.001796062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005418709,"about_ca_system_score_gemma":0.001662602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467005,"about_ca_topic_score_gemma":0.002620071,"domain_scores_codex":[0.9952885,0.002228853,0.0003953284,0.0009026175,0.0008274093,0.0003571961],"domain_scores_gemma":[0.971648,0.01346462,0.002271592,0.009109507,0.001704341,0.00180194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002155539,0.002412085,0.03188771,0.001431751,0.0002983993,0.001244626,0.04913392,0.006308607,0.06476498,0.007519834,0.02858254,0.8042601],"study_design_scores_gemma":[0.001510962,0.005310807,0.08042843,0.001716218,0.001027328,0.004037268,0.03136152,0.2398124,0.1367629,0.07148619,0.4248993,0.001646599],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1955282,0.0004850228,0.7279459,0.001915464,0.0003454166,0.001457732,0.0004990008,0.06145417,0.01036912],"genre_scores_gemma":[0.526974,0.0003589568,0.4611566,0.0005837351,0.0001845584,0.001055276,0.000805036,0.0018414,0.007040428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00740266,"threshold_uncertainty_score":0.03914946,"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."}}