{"id":"W4377204476","doi":"10.59637/jsti.v18i2.220","title":"ANALISIS KESESUAIAN LAHAN UNTUK PENGEMBANGAN LAHAN PERMUKIMAN DENGAN TEKNOLOGI SISTEM INFORMASI GEOGRAFIS (SIG) (STUDI KASUS: KECAMATAN MEDAN TUNTUNGAN)","year":2014,"lang":"id","type":"article","venue":"Jurnal Sains dan Teknologi ISTP","topic":"Coastal Management and Development","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Forestry; Physics; Geography","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","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.002840961,0.001876992,0.001814575,0.0008581319,0.002337927,0.001148555,0.003545797,0.0009200434,0.000793272],"category_scores_gemma":[0.0008376422,0.001769372,0.0008890698,0.002136046,0.001777795,0.00144195,0.004596043,0.002032731,0.002397883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002151783,"about_ca_system_score_gemma":0.0001748059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005849366,"about_ca_topic_score_gemma":0.008597516,"domain_scores_codex":[0.9886514,0.001041833,0.002358653,0.002305955,0.002384301,0.003257839],"domain_scores_gemma":[0.9945557,0.0004273744,0.001384088,0.002199088,0.0001689608,0.001264807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004772483,0.002206291,0.744181,0.0007507192,0.001519405,0.001197332,0.007987974,0.000245636,0.007530678,0.007951981,0.02088152,0.2050702],"study_design_scores_gemma":[0.002223899,0.002042482,0.6637986,0.0003278545,0.0004602083,0.0001597452,0.0120478,0.0006460187,0.002607794,0.0002570034,0.312968,0.002460625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9102175,0.0007985731,0.0004205538,0.003069614,0.001530025,0.001682464,0.00007704362,0.0008630797,0.08134121],"genre_scores_gemma":[0.9853593,0.001234272,0.0007873547,0.001636637,0.0005706579,0.0001252882,0.0002926276,0.0001918534,0.009802049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2920864,"threshold_uncertainty_score":0.9998884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185890137107605,"score_gpt":0.2230410591747432,"score_spread":0.2111821578036671,"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."}}