{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003753167,0.0007312357,0.0005346034,0.0009142957,0.0002704013,0.0005726778,0.000626788,0.0007897896,0.003035511],"category_scores_gemma":[0.0007687379,0.0002304571,0.0004976865,0.0005682585,0.0001927326,0.0008333049,0.0006850091,0.0005909035,0.002352348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005165167,"about_ca_system_score_gemma":0.0003631905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006237803,"about_ca_topic_score_gemma":0.008823565,"domain_scores_codex":[0.9997459,0.00002937014,0.00001015123,0.0001034535,0.00005465641,0.00005649102],"domain_scores_gemma":[0.9998094,0.00003846742,0.00003188234,0.00003603108,0.00006978735,0.00001443483],"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":[0.0003485004,0.0003794984,0.03030613,0.0001222978,0.0001607976,0.0002942298,0.00008916959,0.06250234,0.03432756,0.001905965,0.0141645,0.8553991],"study_design_scores_gemma":[0.000007618772,0.00008906666,0.01274514,0.00003119365,0.00003349853,0.0002458112,0.00006657686,0.9647069,0.01534385,0.00219009,0.004518011,0.00002211387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4791953,0.002163388,0.4850127,0.0007253862,0.0003800076,0.0001375737,0.00350951,0.009898603,0.01897756],"genre_scores_gemma":[0.9072796,0.0003652562,0.07607393,0.0002821302,0.00005563123,0.00005241569,0.003352787,0.00008402245,0.01245425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006237803,"threshold_uncertainty_score":0.01240301,"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."}}