{"id":"W2897980426","doi":"10.1145/3269206.3269313","title":"Predicting Personal Life Events from Streaming Social Content","year":2018,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Event (particle physics); Computer science; Personal life; Relevance (law); Social media; Task (project management); Predictive power; World Wide Web; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001614003,0.00008591307,0.0001165418,0.00003199251,0.0002053062,0.00009139007,0.0003751987,0.00004991251,0.00005648545],"category_scores_gemma":[0.00001765457,0.00007055305,0.00005483401,0.00007303795,0.00001962995,0.000276038,0.0002002549,0.00006577386,0.00003247858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003012706,"about_ca_system_score_gemma":0.00003321668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467908,"about_ca_topic_score_gemma":0.00007181922,"domain_scores_codex":[0.9991489,0.00004359649,0.0001762631,0.00024807,0.0002014603,0.0001816835],"domain_scores_gemma":[0.9996123,0.00003646855,0.00007277173,0.0001412548,0.00006726051,0.00006993745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001993589,0.0003037888,0.5002804,0.00002700643,0.0003504812,0.00001574216,0.03083486,8.148144e-8,0.005670479,0.05693885,0.06544422,0.3401141],"study_design_scores_gemma":[0.00272496,0.001008371,0.6431336,0.0003113385,0.00003938748,0.00002990621,0.005692189,0.2733112,0.02785163,0.01347098,0.03077879,0.00164763],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3753333,0.00002803209,0.6108703,0.001344786,0.0006728989,0.0001198134,0.000005082835,0.0005206004,0.01110517],"genre_scores_gemma":[0.981295,8.65415e-7,0.01710222,0.0004817465,0.0007358322,0.000008949706,0.000001578471,0.000005232571,0.0003685894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6059617,"threshold_uncertainty_score":0.287707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06972244855743445,"score_gpt":0.2738176465067327,"score_spread":0.2040951979492983,"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."}}