{"id":"W4239582258","doi":"10.31219/osf.io/5bzgf","title":"APLIKASI SISTEM INFORMASI GEOGRAFIS (SIG) UNTUK IDENTIFIKASI PERUBAHAN SEMPADAN SUNGAI MUSI DI KOTA PALEMBANG (1922 - 2012)","year":2017,"lang":"id","type":"preprint","venue":"","topic":"Multimedia Learning Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Forestry; Geoprocessing; Humanities; Geography; Physics; Cartography; Art","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":[0.00056837,0.0003460035,0.0002446162,0.002627837,0.0006675886,0.001579547,0.0003251496,0.000265,0.009158066],"category_scores_gemma":[0.003609432,0.0002085104,0.0001967508,0.008685321,0.0004976938,0.0008257712,0.001308159,0.0004649289,0.001355168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001721906,"about_ca_system_score_gemma":0.001970777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1877262,"about_ca_topic_score_gemma":0.2371299,"domain_scores_codex":[0.9996046,0.0000712762,0.0000371195,0.00007570841,0.0001144455,0.00009684701],"domain_scores_gemma":[0.9984237,0.0003875837,0.0002715555,0.000132852,0.0006776585,0.0001065493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001428996,0.00002816641,0.8464471,0.001058929,0.0001117926,0.0006074515,0.01712408,0.0009867911,0.001777338,0.004142268,0.01737652,0.1101967],"study_design_scores_gemma":[0.000006060888,0.00002998485,0.9000764,0.0002286619,0.00006308427,0.0002255767,0.0106854,0.0004304113,0.0007165116,0.0003122283,0.08720919,0.00001643061],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.891098,0.003157834,0.001673147,0.001170335,0.00006369069,0.000139625,0.04915438,0.0002135681,0.05332933],"genre_scores_gemma":[0.9536419,0.001830596,0.002490306,0.00008915049,0.00002275685,0.0001174275,0.02439785,0.00004999287,0.01736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1877262,"threshold_uncertainty_score":0.373267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03153553542266261,"score_gpt":0.2783627679417629,"score_spread":0.2468272325191003,"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."}}