{"id":"W4244223000","doi":"10.1515/iupac.87.0567","title":"Rebound","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Consumer behavior in food and health","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Computer science; Chemistry; Linguistics; Philosophy; Data mining; Organic chemistry","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":[0.002072672,0.002060652,0.001643735,0.005467371,0.001127838,0.004720164,0.003036625,0.002143813,0.133159],"category_scores_gemma":[0.01586739,0.0006652159,0.002658332,0.007288802,0.000542522,0.003445573,0.003350599,0.002830018,0.2059215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002351934,"about_ca_system_score_gemma":0.003575603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02687864,"about_ca_topic_score_gemma":0.0489175,"domain_scores_codex":[0.996518,0.0006089686,0.000625846,0.00103106,0.0007332811,0.0004828568],"domain_scores_gemma":[0.9936531,0.001352314,0.0005850759,0.001926754,0.002032583,0.0004502526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001059319,0.00001939943,0.001633597,0.000803577,0.00003461169,0.00002533048,0.00002238711,0.0001338697,0.00006874521,0.000845468,0.9907945,0.005512535],"study_design_scores_gemma":[0.0001192704,0.000014475,0.002932117,0.0004770228,0.00002251624,0.00006861032,0.00008281196,0.0002112182,0.0001694485,0.0009981021,0.9948854,0.00001900348],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002150107,0.0002738214,0.0001362114,0.0002783831,0.0001795893,0.00003426332,0.9949381,0.0005355109,0.003409112],"genre_scores_gemma":[0.0005361952,0.0001636932,0.0003424127,0.0002558265,0.00003496192,0.000103998,0.9960306,0.0001317304,0.002400393],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.133159,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04816177735754922,"score_gpt":0.5128897829485365,"score_spread":0.4647280055909873,"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."}}