{"id":"W2603612682","doi":"","title":"Temblor, an app focused on your seismic risk and how to reduce it","year":2015,"lang":"en","type":"article","venue":"2015 AGU Fall Meeting","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Tembec","funders":"","keywords":"Geology; Risk analysis (engineering); Forensic engineering; Computer science; Engineering; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001326959,0.0001884818,0.0002224527,0.000111018,0.0002606411,0.0001431881,0.0004779017,0.00009030159,3.839711e-7],"category_scores_gemma":[0.0006485273,0.0001656365,0.00003149699,0.000213253,0.00005406711,0.0003093424,0.0003007742,0.0002023499,0.0001330122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002605172,"about_ca_system_score_gemma":0.00004731798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007179914,"about_ca_topic_score_gemma":0.0002692117,"domain_scores_codex":[0.9984148,0.0002563326,0.0001411133,0.0005435724,0.0002433135,0.0004008306],"domain_scores_gemma":[0.9988665,0.0001469354,0.0001009736,0.0004814788,0.0001176504,0.0002864504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002525781,0.0003069205,0.09653711,0.00004690727,0.0002088436,0.0001737585,0.05807909,0.006010461,0.0008074203,0.00270484,0.2870488,0.5478233],"study_design_scores_gemma":[0.007735171,0.006059487,0.1345055,0.0006161877,0.0001785059,0.0003024575,0.01528041,0.2100855,0.007433018,0.01621574,0.5976537,0.003934304],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9572166,0.00038439,0.020768,0.01604554,0.0006181535,0.0001903217,0.000002805599,0.0002320306,0.004542137],"genre_scores_gemma":[0.978904,0.00002025642,0.0179163,0.001973646,0.0002399566,0.00001575409,0.000001102795,0.00001329387,0.000915705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5438889,"threshold_uncertainty_score":0.6754462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05219631158167213,"score_gpt":0.2836936358252449,"score_spread":0.2314973242435727,"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."}}