{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002942653,0.0001072532,0.0001191174,0.00007453968,0.00005317615,0.0001116034,0.0001942564,0.00007777262,0.000001191712],"category_scores_gemma":[0.00002247629,0.0001014893,0.00001872238,0.0002306328,0.00002689002,0.0005224596,0.0000268272,0.00007575043,0.000006869071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003848794,"about_ca_system_score_gemma":9.034931e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003702599,"about_ca_topic_score_gemma":0.00003787553,"domain_scores_codex":[0.9994712,0.000004743989,0.0001143895,0.0001574112,0.0000608387,0.000191365],"domain_scores_gemma":[0.9997116,0.00006816872,0.00002486597,0.0001387828,0.00002544008,0.00003111566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000122146,0.000002174582,0.0001476865,0.0002167203,0.00001148712,0.000002920152,0.00004752087,0.01062949,0.9834852,0.00006193036,0.00006379167,0.005318879],"study_design_scores_gemma":[0.0001324215,0.00002594055,0.002752075,0.00002225831,0.000007120065,0.000003789452,0.0005563017,0.05345171,0.9425016,0.0001544019,0.0002603727,0.0001319954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787636,0.00004011237,0.01755595,0.00002162315,0.0002132751,0.0001554261,0.00001919154,0.003047625,0.000183209],"genre_scores_gemma":[0.9993857,0.00003144758,0.0003881084,0.000007094217,0.00007906688,0.00005948691,0.000004870135,0.00002855486,0.00001572828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04282222,"threshold_uncertainty_score":0.4138614,"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."}}