{"id":"W2259062574","doi":"10.5539/res.v8n1p157","title":"Evaluation of the Reduction of Tsunami Damages Based on Local Wisdom Contermeasures in Indonesia","year":2016,"lang":"en","type":"article","venue":"Review of European Studies","topic":"Geotechnical and construction materials studies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Direktorat Jenderal Pendidikan Tinggi; Universitas Brawijaya","keywords":"Damages; Countermeasure; Reduction (mathematics); Hazard; Civil engineering; Geology; Environmental science; Geography; Socioeconomics; Business; Geotechnical engineering; Engineering; Mathematics; Law; Political science; Economics; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001506522,0.00008776926,0.0003302343,0.0000397075,0.00001630124,8.21164e-7,0.00008885176,0.00001073809,0.00000901046],"category_scores_gemma":[0.000359954,0.00004483694,0.00006886655,0.0001253132,0.0002615192,0.00002729819,0.00004028458,0.00003161571,0.000002351901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003266097,"about_ca_system_score_gemma":0.000009938536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001641755,"about_ca_topic_score_gemma":0.000001864954,"domain_scores_codex":[0.9986637,0.0004711135,0.0004294516,0.00008172855,0.000292112,0.00006184229],"domain_scores_gemma":[0.9992978,0.00005837187,0.0001422651,0.000186364,0.0003083929,0.000006816387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006961669,0.0001646313,0.003427786,0.02270715,0.0005585089,0.000001303999,0.0003138166,0.01011059,0.1077891,0.0009262479,0.0020689,0.8518623],"study_design_scores_gemma":[0.004163575,0.0004889772,0.6708428,0.1728844,0.001338918,0.00001059943,0.001166488,0.00101137,0.1441069,0.000753591,0.002572404,0.0006599053],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8203681,0.1710354,0.0003577121,0.0007177835,0.0006253917,0.0007723516,0.00002120777,0.00005394466,0.006048122],"genre_scores_gemma":[0.9658662,0.03406255,0.00002024167,0.00001095128,0.00001789673,0.00001029618,1.993885e-7,0.000007447771,0.000004231049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8512024,"threshold_uncertainty_score":0.1828397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04383095027820735,"score_gpt":0.2790408525898613,"score_spread":0.2352099023116539,"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."}}