{"id":"W3127389438","doi":"","title":"PENGOLAHAN CITRA UNTUK IDENTIFIKASI KEMATANGAN BUAH JERUK DENGAN MENGGUNAKAN METODE BACKPROPAGATION BERDASARKAN NILAI HSV","year":2021,"lang":"id","type":"article","venue":"","topic":"Computer Science and Engineering","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Ripeness; Backpropagation; Citrus fruit; Horticulture; Computer science; Artificial intelligence; Biology; Artificial neural network; Ripening","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001478662,0.0008434595,0.0008465497,0.0004951105,0.0008335422,0.003558357,0.002930717,0.0002991285,0.000398624],"category_scores_gemma":[0.0001690241,0.0008922451,0.0004537569,0.003365798,0.0001635926,0.003305788,0.001870279,0.0008004226,0.001191157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003094978,"about_ca_system_score_gemma":0.0007020414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002010218,"about_ca_topic_score_gemma":0.0006280696,"domain_scores_codex":[0.9931199,0.0003664355,0.001181847,0.00218827,0.001551577,0.001591967],"domain_scores_gemma":[0.9954952,0.0002466013,0.0002925041,0.002520276,0.000651101,0.0007943228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005050672,0.002451617,0.002936531,0.002363611,0.001414376,0.006338642,0.04208229,0.012374,0.4141249,0.1892467,0.0569997,0.2696171],"study_design_scores_gemma":[0.001615041,0.0003261928,0.01220063,0.0007037995,0.0002044988,0.001235712,0.001389313,0.5758622,0.3561938,0.002646612,0.04455169,0.003070581],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1480723,0.001615334,0.825567,0.004205845,0.007159869,0.0006118868,0.00001428528,0.0009531149,0.01180038],"genre_scores_gemma":[0.9153919,0.0002768491,0.05758379,0.001127672,0.001132126,0.00003802527,0.00005810124,0.00009960536,0.02429198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7679832,"threshold_uncertainty_score":0.9995865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162984302990276,"score_gpt":0.2301427368319889,"score_spread":0.2138443065329613,"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."}}