{"id":"W4382646338","doi":"10.3390/info14050278","title":"Deep Learning Pet Identification Using Face and Body","year":2023,"lang":"en","type":"article","venue":"Information","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Mitacs","keywords":"Artificial intelligence; Biometrics; Identification (biology); Convolutional neural network; Computer science; Face (sociological concept); Pattern recognition (psychology); Preprocessor; Deep learning; Transfer of learning; Data pre-processing; Matching (statistics); Landmark; Facial recognition system; Machine learning; Medicine; Biology; Pathology","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":[0.0002648261,0.00006177097,0.00004324418,0.0001096257,0.0001790095,0.0001163774,0.00006709175,0.00004615771,0.0000106079],"category_scores_gemma":[0.0001359147,0.00006865617,0.00002117974,0.0001795687,0.00002969204,0.00004767553,0.0000375027,0.00004784762,0.0003127179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008684111,"about_ca_system_score_gemma":0.00001422035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004217681,"about_ca_topic_score_gemma":0.000001348161,"domain_scores_codex":[0.9994279,0.00002867769,0.000234785,0.000103824,0.0001052134,0.00009966668],"domain_scores_gemma":[0.9995678,0.000005913539,0.000136788,0.0001495319,0.0001085537,0.00003140448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002727373,0.00001692621,0.002658025,0.00008459615,0.00003593354,3.798979e-7,0.001346029,0.007932151,0.9643633,0.002935648,0.002409599,0.01819008],"study_design_scores_gemma":[0.0008165672,0.00008589165,0.1103105,0.00002126193,0.00003777137,0.00007555347,0.00461134,0.4312371,0.2457803,0.0001468117,0.2063449,0.0005320084],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9506667,0.00003207662,0.04804115,0.0001424028,0.0002057383,0.000144583,0.000004798952,0.00007244804,0.0006901565],"genre_scores_gemma":[0.9979199,0.0001060281,0.0001748748,0.00005637541,0.00003952572,0.00001435758,0.000966604,0.000006206876,0.0007161638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.718583,"threshold_uncertainty_score":0.401946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670222615576058,"score_gpt":0.2791895079160304,"score_spread":0.2624872817602699,"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."}}