{"id":"W4396229086","doi":"10.61860/jigp.v2i3.61","title":"ANALISIS KRITIS OPTIMALISASI POTENSI DIGITALISASI LAYANAN SESUAI KARAKTERISTIK MASYARAKAT DAN DEMOGRAFI WILAYAH PROVINSI SUMATERA UTARA","year":2024,"lang":"id","type":"article","venue":"JURNAL ILMIAH GEMA PERENCANA","topic":"SMEs Development and Digital Marketing","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Geology","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":[0.002023233,0.0006906241,0.0004965211,0.002045821,0.001223684,0.004262131,0.0007232443,0.001015673,0.02624654],"category_scores_gemma":[0.005155444,0.0003779512,0.0009513574,0.001910881,0.0009504411,0.002097916,0.001582677,0.001366631,0.003946252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001712629,"about_ca_system_score_gemma":0.00213147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0170225,"about_ca_topic_score_gemma":0.03995747,"domain_scores_codex":[0.9985572,0.0002842281,0.00008471071,0.0003011401,0.0006030367,0.000169722],"domain_scores_gemma":[0.9958548,0.001856375,0.0004099015,0.0002870503,0.001422323,0.000169535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.001175878,0.0006906979,0.4779371,0.003957569,0.0006760008,0.001592441,0.03377771,0.01010883,0.05097395,0.02589076,0.01454306,0.3786761],"study_design_scores_gemma":[0.00004168967,0.0008576249,0.6997492,0.001345449,0.0007123735,0.001178211,0.1076475,0.008612952,0.0355691,0.01506461,0.1289842,0.0002370659],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8237519,0.003036051,0.02927289,0.002489957,0.0002283989,0.0004745471,0.004479438,0.000415528,0.1358513],"genre_scores_gemma":[0.9397891,0.00194582,0.01244717,0.0003528811,0.00003130134,0.0002353036,0.001341455,0.0001254405,0.04373166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02624654,"threshold_uncertainty_score":0.08780342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02127456361197223,"score_gpt":0.2760998519105939,"score_spread":0.2548252882986217,"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."}}