{"id":"W4284885420","doi":"10.31544/jtera.v7.i1.2022.25-30","title":"Implementasi Sistem Monitoring Pertumbuhan Tanaman Sawi Hijau Berbasis Pembelajaran Mesin","year":2022,"lang":"id","type":"article","venue":"JTERA (Jurnal Teknologi Rekayasa)","topic":"Computer Science and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Physics; Horticulture; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007194278,0.0009164224,0.0006396387,0.000661305,0.0006413378,0.001800924,0.001006336,0.0008857716,0.009910334],"category_scores_gemma":[0.001450849,0.0003866868,0.0005570358,0.0003960272,0.0003304298,0.001512606,0.0008315021,0.0009051476,0.00430464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005437964,"about_ca_system_score_gemma":0.000727453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002896028,"about_ca_topic_score_gemma":0.002690109,"domain_scores_codex":[0.9993004,0.00007623169,0.00004259019,0.000173635,0.0002998874,0.0001073197],"domain_scores_gemma":[0.9991601,0.0001690559,0.00006736949,0.0001477568,0.0003853142,0.00007046008],"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":[0.001936356,0.000746923,0.02785116,0.001085534,0.0001986336,0.002275406,0.002225823,0.01867806,0.4805692,0.005753262,0.02619293,0.4324867],"study_design_scores_gemma":[0.0002140215,0.00177413,0.04529578,0.0002228497,0.000428162,0.001302157,0.001839803,0.2038711,0.6073601,0.005015498,0.132439,0.0002373504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4887173,0.001566718,0.3830597,0.002019615,0.0008381706,0.001107036,0.002285495,0.05492431,0.06548168],"genre_scores_gemma":[0.8557231,0.0006772742,0.100037,0.0004666698,0.00008180244,0.0003962147,0.001789442,0.00109554,0.03973287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009910334,"threshold_uncertainty_score":0.03315341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03303575466204095,"score_gpt":0.2641324033432976,"score_spread":0.2310966486812566,"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."}}