{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003465878,0.0005844045,0.0005323472,0.002669944,0.002083418,0.004346893,0.0007051794,0.0007251843,0.01972547],"category_scores_gemma":[0.01020831,0.000334168,0.0006718634,0.005257579,0.001122296,0.002610679,0.002401461,0.001424259,0.003131844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003789145,"about_ca_system_score_gemma":0.007250751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06769586,"about_ca_topic_score_gemma":0.09071704,"domain_scores_codex":[0.9966401,0.0007813043,0.0002810221,0.0005146764,0.001374019,0.000408956],"domain_scores_gemma":[0.9913138,0.003261227,0.0008588997,0.0005287568,0.003600417,0.000436924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007462492,0.0002878219,0.513347,0.003438777,0.0004078499,0.001857428,0.0697122,0.002348523,0.005568638,0.01913961,0.0389276,0.3442183],"study_design_scores_gemma":[0.00003152379,0.000268734,0.5419766,0.00140863,0.000342067,0.0007328691,0.1724676,0.001977066,0.005525891,0.005271628,0.2698669,0.0001305397],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8355546,0.004399681,0.008288806,0.00596114,0.0003807458,0.0006889815,0.0123031,0.0004129734,0.1320099],"genre_scores_gemma":[0.9298797,0.002535278,0.006818928,0.0006840196,0.00004948986,0.0004091544,0.005057172,0.0001639216,0.05440247],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06769586,"threshold_uncertainty_score":0.1346036,"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."}}