{"id":"W3006753376","doi":"10.1145/3313831.3376817","title":"Optimizing for Happiness and Productivity: Modeling Opportune Moments for Transitions and Breaks at Work","year":2020,"lang":"en","type":"article","venue":"","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Human multitasking; Task (project management); Context (archaeology); Productivity; Computer science; Happiness; Recommender system; Affect (linguistics); Work (physics); Field (mathematics); Transition (genetics); Human–computer interaction; Cognitive psychology; Psychology; Machine learning; Social psychology; Engineering; Mathematics","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":[0.00180882,0.0006721594,0.0005236504,0.000763648,0.0004770053,0.001593638,0.0008028246,0.001090018,0.001863327],"category_scores_gemma":[0.006799383,0.0004579904,0.0006701953,0.000583197,0.0003092528,0.000984816,0.0004450901,0.0009173392,0.0004240442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047871,"about_ca_system_score_gemma":0.0008039775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02219249,"about_ca_topic_score_gemma":0.0289615,"domain_scores_codex":[0.9995443,0.0001745121,0.00002399922,0.0001413754,0.00003651217,0.00007938037],"domain_scores_gemma":[0.9969329,0.002108782,0.0003693106,0.000159993,0.000193909,0.0002350765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001051724,0.001002751,0.2437998,0.0001466048,0.0002518071,0.0001773782,0.0009546653,0.6559311,0.002532255,0.003403076,0.003344953,0.08740388],"study_design_scores_gemma":[0.00001622747,0.0001260789,0.02042703,0.00001170109,0.00003372569,0.00002337356,0.0001092796,0.9773514,0.0002430747,0.001261239,0.0003822523,0.00001465893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.91345,0.0004356841,0.08132981,0.001004926,0.00004844388,0.00009749808,0.0006887133,0.0003209054,0.002624009],"genre_scores_gemma":[0.9845312,0.00009347541,0.01417472,0.0000336761,0.00001490758,0.00005293044,0.0003531775,0.00001352474,0.0007323565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02219249,"threshold_uncertainty_score":0.04412663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4952627724666583,"score_gpt":0.4221765868056914,"score_spread":0.07308618566096697,"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."}}