{"id":"W4323055189","doi":"10.1109/access.2023.3252499","title":"An Accurate and Fast Animal Species Detection System for Embedded Devices","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science","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":[0.0002041414,0.0005693167,0.0004173693,0.0003689686,0.0002100372,0.0003134481,0.001152204,0.0005440556,0.002094572],"category_scores_gemma":[0.0004550076,0.0002985639,0.0002930782,0.0002058087,0.0001469029,0.0009684233,0.0006121971,0.0003629653,0.0008757455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004617682,"about_ca_system_score_gemma":0.0006360688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001955853,"about_ca_topic_score_gemma":0.003422989,"domain_scores_codex":[0.9998575,0.000009236041,0.00000908757,0.00004188819,0.00005967159,0.00002248925],"domain_scores_gemma":[0.9998356,0.00001901063,0.00002434724,0.00003078199,0.00007550343,0.00001478182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007072159,0.0002657129,0.007608829,0.0006834727,0.0001568787,0.0006339116,0.0001223532,0.03807513,0.4409176,0.003920443,0.02798925,0.4789191],"study_design_scores_gemma":[0.00005959042,0.0005182915,0.006044038,0.00004420481,0.00008052986,0.00053019,0.000038968,0.8174204,0.1523785,0.001260624,0.02156271,0.00006188645],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1332798,0.0009078193,0.8363453,0.0003918218,0.0005664056,0.0002592123,0.0007228644,0.0214169,0.006110038],"genre_scores_gemma":[0.7693048,0.0003904646,0.2187174,0.0005925419,0.00007365159,0.0002047287,0.001311327,0.0001746729,0.00923044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002094572,"threshold_uncertainty_score":0.007007003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03283766162301981,"score_gpt":0.2976125208593661,"score_spread":0.2647748592363462,"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."}}