{"id":"W2982235101","doi":"10.4095/285374","title":"Compilation of digital strong motion data for eastern Canada","year":2010,"lang":"en","type":"report","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Motion (physics); Computer science; Geography; Computer graphics (images); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004890749,0.0007114009,0.0003448003,0.008534423,0.001736911,0.001287031,0.001024693,0.0002012266,0.008725247],"category_scores_gemma":[0.002156615,0.0003289093,0.0002768357,0.01302158,0.0003057626,0.0003811164,0.0009304956,0.0004017332,0.002450363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01251871,"about_ca_system_score_gemma":0.04120069,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.986582,"about_ca_topic_score_gemma":0.9921907,"domain_scores_codex":[0.9991782,0.00001504079,0.00006153774,0.00007750448,0.0005094224,0.0001581711],"domain_scores_gemma":[0.9935523,0.0001150895,0.000230951,0.000295103,0.005440106,0.0003663949],"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":[0.0006613989,0.000282796,0.3203295,0.001508396,0.0003360288,0.001633909,0.002579194,0.01027365,0.01471334,0.005743707,0.2543086,0.3876297],"study_design_scores_gemma":[0.00003750968,0.00003062446,0.6506006,0.0002098506,0.00009788843,0.0001924644,0.001157485,0.002380905,0.004927964,0.0002595645,0.3400182,0.00008695706],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1199004,0.0008150745,0.004191449,0.0001969242,0.00007490862,0.0005575203,0.8384626,0.0007644068,0.03503675],"genre_scores_gemma":[0.1833112,0.001964917,0.01396639,0.000109041,0.0000415559,0.0004400511,0.7656243,0.0003030983,0.03423936],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01341796,"threshold_uncertainty_score":0.09082997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07478264085512096,"score_gpt":0.270927880248699,"score_spread":0.1961452393935781,"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."}}