{"id":"W2782709646","doi":"","title":"Foodie fooderson a conversational agent for the smart kitchen","year":2017,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"AI in Service Interactions","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); University of Victoria","funders":"","keywords":"Watson; Context (archaeology); Computer science; IBM; Recipe; Cognitive computing; Dialog system; Architecture; Recommender system; Human–computer interaction; World Wide Web; Cognition; Multimedia; Artificial intelligence; Psychology","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005988475,0.0001102272,0.00009299233,0.00007462135,0.001271166,0.00115877,0.001852288,0.00002624901,0.000001578341],"category_scores_gemma":[0.0003769227,0.00008682448,0.00004401918,0.0001142266,0.0001438469,0.00131694,0.0006763999,0.00009698689,0.000006475171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005807845,"about_ca_system_score_gemma":0.0001075291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002301987,"about_ca_topic_score_gemma":0.000005657128,"domain_scores_codex":[0.9989484,0.000004830777,0.0001156744,0.0003527527,0.0003025814,0.0002757676],"domain_scores_gemma":[0.9984163,0.0004940382,0.00006922746,0.0007262576,0.000213257,0.00008089039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001332463,0.000103126,0.01140945,0.0002169282,0.0001894274,0.00002182607,0.01110337,0.02937078,0.001024716,0.1073037,0.007325393,0.831918],"study_design_scores_gemma":[0.0001503026,0.00003961005,0.02210967,0.00002681143,0.000005153452,0.00001784086,0.00001257491,0.9652528,0.0002652925,0.0002708376,0.01171708,0.0001320508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004752384,0.00007562782,0.9901698,0.002600813,0.002057221,0.000186005,0.000001883089,0.0001403354,0.00001591333],"genre_scores_gemma":[0.5432244,0.000008713012,0.4557995,0.0006489898,0.0002324423,0.00004964359,5.443317e-7,0.000007580236,0.00002811838],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.935882,"threshold_uncertainty_score":0.9998781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02727232036484973,"score_gpt":0.2532925382559698,"score_spread":0.2260202178911201,"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."}}