{"id":"W4391149359","doi":"10.1109/icacta58201.2023.10393612","title":"Petpaws: A Comprehensive Dataset and Recommender System for Canine and Feline Breeds","year":2023,"lang":"en","type":"article","venue":"","topic":"Food Supply Chain Traceability","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Recommender system; Cosine similarity; Adaptability; Computer science; Selection (genetic algorithm); Breed; Collaborative filtering; Matching (statistics); Similarity (geometry); Information retrieval; Artificial intelligence; Pattern recognition (psychology); Statistics; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000202038,0.00009575955,0.0001517196,0.000008061876,0.0001393959,0.00003857273,0.00007693628,0.00004901748,0.00005932734],"category_scores_gemma":[0.00003008597,0.00003344963,0.00001909884,0.0001586638,0.00005377107,0.00005645895,0.0001084655,0.00004338688,0.000008749667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001145937,"about_ca_system_score_gemma":0.000002511044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007502222,"about_ca_topic_score_gemma":0.001803368,"domain_scores_codex":[0.9992931,0.00003980211,0.0001378761,0.0002742308,0.00006731004,0.0001876191],"domain_scores_gemma":[0.9993073,0.000485329,0.0000264588,0.0000500791,0.00004466422,0.0000861796],"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":[0.0003736295,0.0001880079,0.03918322,0.0007932748,0.000150485,0.00002471331,0.0003972587,0.00001308841,0.1002721,0.001874537,0.7313029,0.1254268],"study_design_scores_gemma":[0.001655453,0.001872967,0.4388536,0.00006739834,0.00006443199,0.0001254488,0.01877579,0.009073483,0.001421455,0.000712448,0.5265703,0.000807195],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985044,0.0001120617,0.0000055127,0.009899044,0.00007682862,0.000411634,0.004222052,0.0001564264,0.00007246452],"genre_scores_gemma":[0.996288,0.00002940025,0.0002261103,0.0003541579,0.0001010674,0.00003644323,0.00279434,7.66344e-7,0.0001697411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3996704,"threshold_uncertainty_score":0.1364036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05335668839490058,"score_gpt":0.2599955484884615,"score_spread":0.2066388600935609,"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."}}