{"id":"W2957655889","doi":"10.1088/1742-6596/1237/3/032045","title":"Detection and Recognition of Flower Image Based on SSD network in Video Stream","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Canadian Patient Safety Institute","keywords":"Pascal (unit); Computer science; Artificial intelligence; Artificial neural network; Computer vision; Object detection; Field (mathematics); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001594287,0.000399937,0.0003402981,0.0007127285,0.0001393944,0.0002823222,0.0003419187,0.0002953288,0.001269899],"category_scores_gemma":[0.0003464461,0.00009694248,0.0002880083,0.000433467,0.0001311352,0.0003529371,0.000230242,0.000208414,0.0003021461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003552789,"about_ca_system_score_gemma":0.0002714818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00504663,"about_ca_topic_score_gemma":0.005102371,"domain_scores_codex":[0.9998703,0.000008743936,0.00000672332,0.00004246041,0.0000466147,0.00002522177],"domain_scores_gemma":[0.9999096,0.00001643374,0.000007463731,0.000007809565,0.00004922048,0.000009408516],"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":[0.0008958988,0.0002671262,0.01992006,0.000191241,0.00008761758,0.0006237096,0.0001109826,0.05135284,0.2173678,0.001054216,0.006784583,0.7013441],"study_design_scores_gemma":[0.00001181141,0.0001248957,0.01454876,0.000009081542,0.00002675723,0.0002007342,0.00004708704,0.9343736,0.04931143,0.0003508251,0.0009795181,0.0000155603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7397062,0.0007905609,0.2498566,0.0002849541,0.0002616084,0.0001140898,0.001163186,0.00203986,0.005782836],"genre_scores_gemma":[0.9506038,0.0003128392,0.04384543,0.00008063091,0.0000354016,0.00003888955,0.001055883,0.00002207257,0.004005117],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00504663,"threshold_uncertainty_score":0.0100345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013014314960594,"score_gpt":0.1934933107642735,"score_spread":0.1804789958036795,"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."}}