{"id":"W2091004324","doi":"10.1016/j.cageo.2007.05.013","title":"Application of DInSAR-GPS optimization for derivation of three-dimensional surface motion of the southern California region along the San Andreas fault","year":2007,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"earthquake and tectonic studies","field":"Earth and Planetary Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; California Institute of Technology; Jet Propulsion Laboratory; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Geology; Global Positioning System; Geodesy; Motion (physics); Seismology; Surface (topology); Fault (geology); San andreas fault; Remote sensing; Computer science; Geometry; Artificial intelligence; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0002912226,0.0005312959,0.000466752,0.0004313048,0.0004504702,0.0005169759,0.0004651663,0.0004829339,0.00201939],"category_scores_gemma":[0.001012569,0.0004738442,0.0004244699,0.0004240119,0.0001967259,0.0002210373,0.0003066541,0.0003507376,0.0002439082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005060344,"about_ca_system_score_gemma":0.001731942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06227129,"about_ca_topic_score_gemma":0.05599907,"domain_scores_codex":[0.9999402,0.00001668204,0.000004562249,0.00001368834,0.00001560035,0.000009302771],"domain_scores_gemma":[0.9998431,0.00006439637,0.00001714286,0.00001725554,0.00004883657,0.000009199147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001806328,0.00001726625,0.001351322,0.00001980371,0.00001464465,0.00005982468,0.00002586399,0.9876961,0.0006277495,0.0009586978,0.0004130477,0.008797526],"study_design_scores_gemma":[0.00001010222,0.000006009507,0.0006353006,0.000002814338,0.000003694752,0.000005982916,0.00001080924,0.9984042,0.0002526792,0.0002411919,0.0004241556,0.000003083703],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4965735,0.0004904213,0.4652974,0.000477323,0.0001271576,0.0001474196,0.001671282,0.002341237,0.03287434],"genre_scores_gemma":[0.9131378,0.0001372391,0.0829801,0.00005457901,0.00002321037,0.00007036156,0.0006933974,0.0002386388,0.002664688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06227129,"threshold_uncertainty_score":0.1238177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01764673021793257,"score_gpt":0.2117834117936105,"score_spread":0.1941366815756779,"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."}}