{"id":"W4206325412","doi":"10.5958/2278-4853.2021.00839.9","title":"An overview of deep learning in agriculture","year":2021,"lang":"en","type":"article","venue":"Asian Journal of Multidimensional Research","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Agriculture; Artificial intelligence; Computer science; Geography; Archaeology","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":[0.0007399516,0.0007092333,0.0005030037,0.001209968,0.0002757314,0.001482029,0.0008724145,0.001515738,0.004437012],"category_scores_gemma":[0.00109705,0.0004439728,0.0005636055,0.002215125,0.0005706844,0.002041818,0.001096947,0.001912113,0.002266075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001208672,"about_ca_system_score_gemma":0.0009242504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003087864,"about_ca_topic_score_gemma":0.002296416,"domain_scores_codex":[0.9996853,0.00006291061,0.00003166048,0.00006279563,0.0001262895,0.00003095332],"domain_scores_gemma":[0.9996279,0.0001915843,0.0000220654,0.00002902,0.0001044484,0.00002490421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006102574,0.0001063491,0.001217889,0.001819461,0.00008688575,0.0001415256,0.00008712006,0.03976509,0.002769515,0.09209643,0.02908639,0.8327624],"study_design_scores_gemma":[0.00001998831,0.0001852432,0.001780405,0.001563725,0.00005706957,0.000404468,0.00008766676,0.133844,0.004539504,0.1777705,0.679665,0.00008250773],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.004666032,0.3789695,0.5515547,0.006838444,0.001340503,0.0001120587,0.0006269402,0.001037538,0.05485436],"genre_scores_gemma":[0.1015606,0.5677179,0.2781523,0.003493549,0.00253166,0.0002858619,0.001551872,0.0003525428,0.04435369],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004437012,"threshold_uncertainty_score":0.01484323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09651992612560321,"score_gpt":0.3664140749092138,"score_spread":0.2698941487836106,"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."}}