{"id":"W3210085046","doi":"10.3390/sym13112073","title":"Categorizing Diseases from Leaf Images Using a Hybrid Learning Model","year":2021,"lang":"en","type":"article","venue":"Symmetry","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Cluster analysis; Identification (biology); Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004514945,0.0001378919,0.0001728455,0.000007085188,0.0002834665,0.0001257806,0.0001274148,0.00004968982,0.0002331103],"category_scores_gemma":[0.000081494,0.00005175668,0.0001273568,0.0002779305,0.00002418119,0.0001657899,0.0001115454,0.0001616417,0.00003970792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002302584,"about_ca_system_score_gemma":0.00001559357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003819135,"about_ca_topic_score_gemma":0.00005407092,"domain_scores_codex":[0.999028,0.00006113987,0.0001390742,0.0003342728,0.0001770798,0.0002604934],"domain_scores_gemma":[0.9995987,0.0001221867,0.00005458063,0.00004884328,0.00006858003,0.0001070397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007653778,0.00008417969,0.04540889,0.000005352133,0.00003243332,0.00007732886,0.00004320624,0.000280565,0.9278666,0.0001661143,0.003123816,0.02290388],"study_design_scores_gemma":[0.0008461248,0.0001997205,0.3767554,0.0002477533,0.0004542928,0.0001213029,0.005753479,0.03357985,0.5146589,0.02310312,0.041919,0.002361045],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960353,0.001740765,0.0001315109,0.000482638,0.0001486734,0.00004206704,0.00009470589,0.0001038777,0.001220398],"genre_scores_gemma":[0.9972749,0.00004926492,0.0005654963,0.0003130672,0.0007128734,0.000002831661,0.0003204821,0.000001353564,0.0007597663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4132077,"threshold_uncertainty_score":0.2552394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01904170414180975,"score_gpt":0.2185982195256411,"score_spread":0.1995565153838313,"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."}}