{"id":"W4381956525","doi":"10.1007/978-3-031-36272-9_64","title":"C$$^{2}$$Tutor: Helping People Learn to Avoid Present Bias During Decision Making","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Perfectionism, Procrastination, Anxiety Studies","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Northern Alberta Institute of Technology; University of Alberta","funders":"","keywords":"Procrastination; TUTOR; Harm; Computer science; Cognition; Term (time); Human–computer interaction; Cognitive bias; Applied psychology; Psychology; Social psychology; Psychiatry","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":[0.0003476857,0.0006332446,0.0002868852,0.0003032491,0.0004265491,0.001096237,0.0007376331,0.0008578978,0.1097512],"category_scores_gemma":[0.001415306,0.0001565402,0.0002122283,0.0002137552,0.0002892767,0.001195207,0.0007902243,0.001274589,0.06511234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002607922,"about_ca_system_score_gemma":0.0004811232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005398464,"about_ca_topic_score_gemma":0.002164647,"domain_scores_codex":[0.9998965,0.00003488721,0.000003459429,0.00001405018,0.00003645265,0.0000145925],"domain_scores_gemma":[0.999572,0.0002196357,0.00001738245,0.00002490564,0.00007081735,0.00009525018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00004796217,0.0001679558,0.0001585606,0.0001196873,0.000004973662,0.00006567541,0.0004107678,0.0001578079,0.001375277,0.005404915,0.5682727,0.4238137],"study_design_scores_gemma":[0.00005830554,0.000212715,0.002109181,0.0002260452,0.00001857124,0.0005554359,0.000500278,0.002925461,0.003293046,0.02491811,0.9651524,0.0000304011],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01273321,0.006407808,0.1129054,0.0139883,0.00667067,0.0002517993,0.0005991814,0.009987181,0.8364566],"genre_scores_gemma":[0.03717312,0.002536045,0.06238638,0.003905592,0.0007855993,0.0002343107,0.000536777,0.001008267,0.8914339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1097512,"threshold_uncertainty_score":0.3671543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04256456804839616,"score_gpt":0.3230263116184591,"score_spread":0.280461743570063,"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."}}