{"id":"W2563499634","doi":"10.5539/ass.v13n1p161","title":"Natural Disaster Mitigation through Integrated Social Learning Science in Primary School","year":2016,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Education and Critical Thinking Development","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics education; Syllabus; Natural disaster; Descriptive statistics; Test (biology); Psychology; Qualitative property; Theme (computing); Computer science; Geography; Mathematics; Statistics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001108562,0.0002724319,0.0003119793,0.0009280099,0.001015315,0.001701676,0.0005246383,0.0004885738,0.003164468],"category_scores_gemma":[0.001478323,0.0001643278,0.0003989188,0.0003936238,0.0006047808,0.0008925322,0.001846588,0.0005362585,0.0007913939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009973951,"about_ca_system_score_gemma":0.002910671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009208912,"about_ca_topic_score_gemma":0.00233013,"domain_scores_codex":[0.9992601,0.0002540571,0.0000310887,0.0001191397,0.0002001027,0.000135485],"domain_scores_gemma":[0.9992167,0.0002203806,0.00009061532,0.00008688963,0.0001369005,0.0002485261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003333109,0.02093158,0.1661467,0.001510759,0.00008593696,0.001542785,0.05310952,0.003798471,0.01949635,0.017451,0.007942452,0.7076511],"study_design_scores_gemma":[0.0005585662,0.01757501,0.5803722,0.002037063,0.0002752495,0.001883768,0.1088174,0.0146546,0.02471367,0.04200833,0.2069819,0.0001224462],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695267,0.0002246581,0.006458295,0.0007566412,0.00002955998,0.0004549127,0.00005854847,0.0001055914,0.02238515],"genre_scores_gemma":[0.9831567,0.0002921878,0.01189129,0.0001398828,0.00001114876,0.0002328505,0.0000687326,0.00000608456,0.004201103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003164468,"threshold_uncertainty_score":0.0105862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576097522844577,"score_gpt":0.3305976476898188,"score_spread":0.314836672461373,"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."}}