{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00171107,0.0006316777,0.0007374384,0.001754295,0.0003316907,0.001843393,0.000885041,0.001374104,0.001679807],"category_scores_gemma":[0.001701464,0.0003409788,0.0006176299,0.001956724,0.0008223118,0.002474868,0.0007554751,0.001738431,0.001098086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008991765,"about_ca_system_score_gemma":0.001010829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001652248,"about_ca_topic_score_gemma":0.0009581635,"domain_scores_codex":[0.9991212,0.0001788494,0.0000681561,0.0001820858,0.0003721518,0.00007743217],"domain_scores_gemma":[0.9988462,0.0004015836,0.00007264202,0.00006891423,0.0005640967,0.00004659987],"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":[0.0000671135,0.00009714242,0.001716375,0.001256095,0.00007879424,0.0001967764,0.0001955012,0.006808333,0.0115219,0.03290932,0.01167132,0.9334813],"study_design_scores_gemma":[0.00003833023,0.0004472218,0.009958927,0.001284255,0.0001867766,0.001092044,0.0005017869,0.1310592,0.03164514,0.085982,0.7375506,0.0002536371],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01588039,0.462862,0.4650631,0.007040294,0.002548808,0.0001032898,0.0002042479,0.001053222,0.04524462],"genre_scores_gemma":[0.2357117,0.3971949,0.3397979,0.002604994,0.00359093,0.0001559227,0.0006236645,0.0001660372,0.0201541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001843393,"threshold_uncertainty_score":0.009049118,"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."}}