{"id":"W2613316896","doi":"10.15273/ijge.2017.01.003","title":"AUTOMATED DATA PROCESSING AND INTEGRATION OF LARGE MULTIPLE DATA SOURCES IN GEOHAZARDS MONITORING","year":2017,"lang":"en","type":"article","venue":"International Journal of Georesources and Environment","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chengdu University; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection; Chengdu University of Technology","keywords":"Geohazard; Automation; Computer science; Data integration; Data processing; Real-time computing; Data mining; Engineering; Database","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":[0.0006307715,0.00007324929,0.0001371005,0.00008386374,0.0001348752,0.0001383782,0.001468437,0.00002760366,0.000002442496],"category_scores_gemma":[0.0001332998,0.00005791361,0.00001041614,0.00001286701,0.0001252388,0.0008780584,0.001722137,0.0001027941,4.413154e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001133511,"about_ca_system_score_gemma":0.00001107981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008677938,"about_ca_topic_score_gemma":0.00002561844,"domain_scores_codex":[0.9991804,0.00002829321,0.000268878,0.0001830822,0.0002440682,0.00009528978],"domain_scores_gemma":[0.9990944,0.0000583343,0.0003934929,0.000394923,0.00002749219,0.00003139018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007173658,0.0001269142,0.7369911,0.00001273708,0.0001031765,0.00004996431,0.002427948,0.0004155095,0.0005211752,0.00009940877,0.00005730771,0.259123],"study_design_scores_gemma":[0.0006560867,0.00006123774,0.8533064,0.0001173546,0.000009211678,0.0000542125,0.0003423212,0.1424324,0.0002344801,0.0002232212,0.002503547,0.00005958682],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856598,0.001812158,0.01091248,0.001356625,0.0001735878,0.00002973922,0.00001930491,0.000006640064,0.0000296044],"genre_scores_gemma":[0.992246,0.001000869,0.006612117,0.00002331176,0.00009936855,4.347144e-7,0.000006104451,0.000002711276,0.000009136657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2590635,"threshold_uncertainty_score":0.2728747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04546299384126123,"score_gpt":0.3085635637370094,"score_spread":0.2631005698957482,"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."}}