{"id":"W4288265098","doi":"10.5281/zenodo.3946728","title":"Insights into Toronto's Foodservice Market using Data Science Tools","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Wine Industry and Tourism","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Marketing; Data science; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00103177,0.0001862472,0.0001570451,0.003125021,0.0006110176,0.002936423,0.0004128831,0.0002438133,0.006796495],"category_scores_gemma":[0.004849417,0.0001216818,0.0002197415,0.005539256,0.0006533039,0.0009861083,0.0007436614,0.0004698367,0.0005547418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005652447,"about_ca_system_score_gemma":0.002985598,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4219093,"about_ca_topic_score_gemma":0.6098409,"domain_scores_codex":[0.9993654,0.0001508571,0.00004048852,0.00006894761,0.0003299656,0.00004426003],"domain_scores_gemma":[0.9956797,0.002348378,0.0006037118,0.0002671994,0.0008669465,0.0002340281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004225058,0.0001676602,0.6538444,0.001070839,0.0002079909,0.001981943,0.02110446,0.02153959,0.007264615,0.1064693,0.05975813,0.1261686],"study_design_scores_gemma":[0.00002094616,0.00009273184,0.7989139,0.0002699402,0.00005804073,0.0001681196,0.03335295,0.06481692,0.003561608,0.0151651,0.08348942,0.00009036938],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8528075,0.0008480026,0.02010616,0.005358003,0.00007493598,0.0002224992,0.05721505,0.0005935787,0.06277426],"genre_scores_gemma":[0.9746099,0.0003855807,0.01209723,0.0001220727,0.00002032192,0.00004917312,0.008553743,0.00004697531,0.004114937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5780907,"threshold_uncertainty_score":0.8389071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06928046603073043,"score_gpt":0.2633951020349092,"score_spread":0.1941146360041787,"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."}}