{"id":"W2414109967","doi":"10.1145/2901790.2901805","title":"Designing for Advanced Personalization in Personal Task Management","year":2016,"lang":"en","type":"article","venue":"","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Personalization; Computer science; Task (project management); Scripting language; Human–computer interaction; Process (computing); Task management; Mechanism (biology); World Wide Web; Software engineering; Multimedia; Programming language; Systems engineering; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001337766,0.0001069392,0.0001314432,0.0004146206,0.00009404469,0.0001235742,0.0003207157,0.00003213241,0.001148063],"category_scores_gemma":[0.0001467927,0.00006335178,0.0000860791,0.0003643906,0.00002308289,0.001014752,0.00006307083,0.00002256194,0.0003664377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007367367,"about_ca_system_score_gemma":0.00001267447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002058961,"about_ca_topic_score_gemma":0.000017884,"domain_scores_codex":[0.9982793,0.0000365413,0.0004226262,0.0002942197,0.0007254878,0.0002417897],"domain_scores_gemma":[0.9992507,0.0003151632,0.0001117676,0.0001558166,0.0001112866,0.00005529448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003266401,0.0001270239,0.04974301,0.00003575265,0.00002945736,0.000009159938,0.002991115,0.00006352626,0.009122326,0.09718245,0.04710139,0.7932681],"study_design_scores_gemma":[0.01874276,0.0004736515,0.275676,0.0003594939,0.00009468805,0.000008031525,0.04389289,0.03061779,0.005332004,0.04526313,0.5776145,0.001925025],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1138343,0.00002566861,0.8638133,0.002640222,0.0002160955,0.0007343282,0.00001577114,0.00007059141,0.01864973],"genre_scores_gemma":[0.9455972,0.00001350173,0.01572584,0.0006009409,0.00002890882,0.0001367084,0.00000679789,0.000008447964,0.03788169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8480875,"threshold_uncertainty_score":0.999765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2194462722949459,"score_gpt":0.4276051207220872,"score_spread":0.2081588484271413,"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."}}