{"id":"W4380484899","doi":"10.3390/app13127052","title":"Progress of Machine Vision Technologies in Intelligent Dairy Farming","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Effects of Environmental Stressors on Livestock","field":"Agricultural and Biological Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Science Foundation of Inner Mongolia; China Scholarship Council; Inner Mongolia Agricultural University; National Natural Science Foundation of China","keywords":"Artificial intelligence; Dairy cattle; Machine vision; Lameness; Engineering; Computer science; Medicine; Animal science; Biology","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":[0.0004191754,0.00009284231,0.000130735,0.00004816828,0.000126022,0.00002401938,0.0004760228,0.00005628527,0.00003016513],"category_scores_gemma":[0.00002516396,0.0000348446,0.00002875746,0.001165276,0.0006174452,0.00008283718,0.0002677037,0.00007813727,0.00004178094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001364674,"about_ca_system_score_gemma":0.000002303682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006146169,"about_ca_topic_score_gemma":0.0001485232,"domain_scores_codex":[0.9989908,0.00001861427,0.0001754375,0.0002903418,0.0002759399,0.0002488754],"domain_scores_gemma":[0.9996569,0.0001918651,0.00007776279,0.00004706081,0.000003860542,0.00002251766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000124521,0.00009784621,0.08851469,0.00001085664,0.000002275085,0.000003897414,0.00009799685,0.0001517865,0.3648238,0.001797402,0.00006140228,0.5444255],"study_design_scores_gemma":[0.0001807622,0.0009373091,0.7077531,0.0001127205,0.000004774522,0.000002631753,0.005720318,0.001175746,0.2735952,0.008081911,0.002052319,0.0003831865],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972966,0.000246271,0.000001063023,0.000774056,0.00003542564,0.0002447183,0.000005675453,0.0001502109,0.001246009],"genre_scores_gemma":[0.9995761,0.00006829257,0.0002637452,0.00001301434,0.000009648053,0.00003610264,0.000006736126,5.070466e-7,0.00002585324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6192384,"threshold_uncertainty_score":0.2275002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01741032876590411,"score_gpt":0.2575690609920369,"score_spread":0.2401587322261327,"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."}}