{"id":"W3133599571","doi":"10.3390/rs13050943","title":"Hair Fescue and Sheep Sorrel Identification Using Deep Learning in Wild Blueberry Production","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lethbridge College; University of Prince Edward Island; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Sprayer; Deep learning; Pattern recognition (psychology); Agronomy; Biology","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":[0.0002683135,0.0004441572,0.0002261407,0.0005239787,0.0001946664,0.0002310813,0.0002677786,0.0002901517,0.0005556687],"category_scores_gemma":[0.0002882047,0.0001354905,0.0001887872,0.0002091308,0.0001489129,0.0002716698,0.0001991776,0.0001528485,0.000199384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004447109,"about_ca_system_score_gemma":0.0002013055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03021399,"about_ca_topic_score_gemma":0.06167594,"domain_scores_codex":[0.9999074,0.00001192437,0.000002916596,0.00003729602,0.00001595386,0.00002454452],"domain_scores_gemma":[0.999911,0.00002438721,0.00001349374,0.000007650348,0.00003162073,0.00001191675],"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":[0.001018966,0.000545553,0.2875924,0.0003611209,0.0002108186,0.003079066,0.0006302987,0.07520265,0.2457635,0.0003491253,0.004230403,0.381016],"study_design_scores_gemma":[0.00002747305,0.000473265,0.4217583,0.00004880713,0.00007653013,0.0004893673,0.000906329,0.5279495,0.04459048,0.0003812897,0.003267295,0.00003125021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963074,0.0001841979,0.002675528,0.00004457202,0.000007889999,0.000008494195,0.0001439623,0.0001075465,0.0005205739],"genre_scores_gemma":[0.991735,0.0001200205,0.006100905,0.00003337302,0.000005571801,0.000008045468,0.000525258,0.00001057649,0.001461254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03021399,"threshold_uncertainty_score":0.06007624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0164802216190601,"score_gpt":0.2153176651439725,"score_spread":0.1988374435249124,"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."}}