{"id":"W2609169714","doi":"10.1016/j.jvolgeores.2017.04.017","title":"Multidimensional Small Baseline Subset (MSBAS) for volcano monitoring in two dimensions: Opportunities and challenges. Case study Piton de la Fournaise volcano","year":2017,"lang":"en","type":"article","venue":"Journal of Volcanology and Geothermal Research","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"Canadian Space Agency","keywords":"Geology; Volcano; GNSS applications; Seismology; Dike; Geodesy; Interferometric synthetic aperture radar; Deformation (meteorology); GNSS augmentation; Baseline (sea); Synthetic aperture radar; Remote sensing; Global Positioning System; Petrology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003998894,0.0001724164,0.0003357629,0.0003934479,0.0004311411,0.0000587293,0.0002258382,0.0001330795,0.00001236332],"category_scores_gemma":[0.000564361,0.0001477186,0.00004493075,0.00005368133,0.000393939,0.0001452807,0.0001169341,0.0006611455,4.44556e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008071183,"about_ca_system_score_gemma":0.0001347173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006160761,"about_ca_topic_score_gemma":0.0005193875,"domain_scores_codex":[0.9984881,0.0003182803,0.0003919404,0.0001923378,0.0002029667,0.0004064018],"domain_scores_gemma":[0.9976131,0.001483897,0.0001227054,0.0003062862,0.0002865064,0.0001874844],"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":[0.0004521107,0.0006619252,0.04307349,0.0002473059,0.0003020662,0.007213855,0.003448422,0.0001031079,0.003047238,0.002039172,0.000375815,0.9390355],"study_design_scores_gemma":[0.02696965,0.006324024,0.2623947,0.002857679,0.0005152972,0.06406734,0.07702129,0.07360926,0.0213468,0.0118441,0.4504106,0.002639237],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874911,0.008938447,0.00161592,0.001206356,0.0001057343,0.0003864709,0.00001029838,0.00002479424,0.0002208416],"genre_scores_gemma":[0.9716536,0.01184642,0.01612365,0.00001363327,0.0002170232,0.00005238045,5.892958e-7,0.00003508542,0.00005764939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9363962,"threshold_uncertainty_score":0.6023791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1504313269123643,"score_gpt":0.3960322949004101,"score_spread":0.2456009679880458,"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."}}