{"id":"W2272787425","doi":"10.5958/0976-4038.2015.00026.3","title":"Weed Identification using Ultrasonic Sensor in Labview","year":2015,"lang":"en","type":"article","venue":"International Journal of Bio-resource and Stress Management","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Department of Agriculture","funders":"","keywords":"Ultrasonic sensor; Weed; Identification (biology); Acoustics; Environmental science; Computer science; Remote sensing; Biology; Geography; Agronomy; Physics; Ecology","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":[0.0008530067,0.001303546,0.0008321849,0.001058359,0.0002354609,0.0009661382,0.001200538,0.0005169225,0.02831996],"category_scores_gemma":[0.00106529,0.0005716188,0.0004981257,0.0006120722,0.0002906212,0.0006596426,0.0005508277,0.0009529269,0.0072829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002270542,"about_ca_system_score_gemma":0.0004412264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005609917,"about_ca_topic_score_gemma":0.0005865034,"domain_scores_codex":[0.999348,0.0001055897,0.00005750496,0.0001651483,0.0002540024,0.00006973273],"domain_scores_gemma":[0.9994926,0.0001208597,0.00005271865,0.0000714098,0.0002229996,0.00003930182],"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":[0.001401897,0.0004648201,0.00453673,0.002186548,0.000257715,0.0005850469,0.001041199,0.02161003,0.3599502,0.006918439,0.06720585,0.5338415],"study_design_scores_gemma":[0.0005680217,0.0008898735,0.009370794,0.0002325004,0.0002557434,0.001482188,0.0002289862,0.377807,0.3747735,0.003904123,0.2301233,0.0003639324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01563057,0.0002252869,0.9017825,0.0001099283,0.0001731679,0.0003213148,0.001808838,0.06744806,0.01250033],"genre_scores_gemma":[0.2918023,0.0006569166,0.6664072,0.0004485345,0.0001097038,0.003116308,0.006072332,0.006417291,0.02496951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02831996,"threshold_uncertainty_score":0.09473968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02829522124036005,"score_gpt":0.2539544404525533,"score_spread":0.2256592192121933,"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."}}