{"id":"W4386208007","doi":"10.1109/icmew59549.2023.00090","title":"Aerial Sensor Data Guided Object Detection for Cattle Monitoring in Open Fields","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Object detection; Artificial intelligence; Drone; Computer vision; RGB color model; Transfer of learning; Field (mathematics); Sensor fusion; Change detection; Real-time computing; Data mining; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003157342,0.0004145223,0.0003537686,0.001083445,0.0001995525,0.0003369006,0.0005228751,0.0003884586,0.0007392598],"category_scores_gemma":[0.0004549309,0.0001375008,0.0002208292,0.0005806034,0.0001986824,0.000461555,0.0003566239,0.0003301848,0.0003391163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002761208,"about_ca_system_score_gemma":0.0002984876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004241239,"about_ca_topic_score_gemma":0.009823661,"domain_scores_codex":[0.9998019,0.00002862186,0.000006113679,0.00005843371,0.0000554818,0.00004938015],"domain_scores_gemma":[0.9997943,0.00006043372,0.00003406517,0.00003066262,0.00006109148,0.00001940436],"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":[0.0006631346,0.0006416959,0.04347805,0.0002628115,0.0002119534,0.0004027343,0.0003325156,0.08650754,0.1881533,0.0009715996,0.006132815,0.6722419],"study_design_scores_gemma":[0.00002118089,0.0001641694,0.03896119,0.00002214185,0.0000437581,0.0001712196,0.0002962642,0.9224803,0.03445226,0.001018203,0.002350984,0.0000183755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7734007,0.001327438,0.2178845,0.0002180288,0.00009097273,0.00007586341,0.0007415203,0.002223656,0.004037299],"genre_scores_gemma":[0.944932,0.0002326622,0.05213517,0.00007446622,0.00003383226,0.00001982907,0.001064982,0.0000261252,0.001480909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004241239,"threshold_uncertainty_score":0.008433104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1426657503365037,"score_gpt":0.3261271916114484,"score_spread":0.1834614412749447,"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."}}