{"id":"W4389883100","doi":"10.3390/electronics12245044","title":"MCFP-YOLO Animal Species Detector for Embedded Systems","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Dataflow; Computer science; Detector; Power consumption; Real-time computing; Frame rate; Frame (networking); Embedded system; Power (physics); Artificial intelligence; Parallel computing; Telecommunications","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":[0.0003875528,0.0008916074,0.0003852151,0.0005892882,0.0002892483,0.000644702,0.001437788,0.0006773275,0.004843216],"category_scores_gemma":[0.001354667,0.0004130373,0.0003755519,0.0002936703,0.000304384,0.001275453,0.0006776407,0.0008340108,0.001540784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008410328,"about_ca_system_score_gemma":0.001280851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00474611,"about_ca_topic_score_gemma":0.007958949,"domain_scores_codex":[0.9997746,0.00002007685,0.00001063107,0.00006593947,0.00009592179,0.00003277882],"domain_scores_gemma":[0.9996243,0.0001129322,0.0000521054,0.00006628354,0.0001191364,0.00002531442],"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":[0.0009892515,0.0003366165,0.007681497,0.0007791109,0.0001672579,0.000546308,0.0002008657,0.2135647,0.1768065,0.02420322,0.06311733,0.5116073],"study_design_scores_gemma":[0.00002154718,0.0000972437,0.0007695311,0.00002206102,0.00001587669,0.00009408443,0.00001566322,0.9557719,0.02772028,0.003315065,0.01213644,0.00002026975],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0179687,0.0004580785,0.9623418,0.0003525059,0.0001252595,0.0001334777,0.0005345966,0.0145987,0.003486787],"genre_scores_gemma":[0.3608845,0.0005426775,0.624454,0.0007306926,0.0001038128,0.0004051112,0.001884345,0.0007068523,0.01028807],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004843216,"threshold_uncertainty_score":0.01620215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02182293905330532,"score_gpt":0.2193746708254629,"score_spread":0.1975517317721575,"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."}}