{"id":"W2187451558","doi":"","title":"An assessment of DInSAR potential for simulating geological subsurface structure","year":2013,"lang":"en","type":"article","venue":"Congress on Modelling and Simulation","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Geology; Deformation (meteorology); Remote sensing; Interferometry; Subsidence; Satellite; Deformation monitoring; Geodesy; Interferometric synthetic aperture radar; GNSS augmentation; Geophysics; Seismology; Synthetic aperture radar; Structural basin; Geomorphology","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":[0.00006949496,0.0001150821,0.0001523081,0.00004319752,0.00007835461,0.00003293989,0.00006204889,0.0001178245,0.00002213503],"category_scores_gemma":[0.000006966546,0.00009834837,0.00003704925,0.00003912532,0.0000280953,0.00009752015,0.000006373193,0.00008147577,2.89463e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001405589,"about_ca_system_score_gemma":0.000004251729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009359136,"about_ca_topic_score_gemma":4.724871e-7,"domain_scores_codex":[0.9994106,0.00001519716,0.0001983063,0.0001636947,0.00009641659,0.0001157936],"domain_scores_gemma":[0.9994833,0.0001673754,0.00004775752,0.0001618162,0.00009760947,0.00004212144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003945302,0.00001464145,0.0002413868,0.00002429356,0.00001068152,6.621743e-8,0.00002835442,0.9527244,0.00110344,0.0009642314,0.000003545581,0.044881],"study_design_scores_gemma":[0.0001698346,0.0000619323,0.0003854017,0.00001970046,0.00001541755,3.168592e-7,0.0000157302,0.992137,0.001904369,0.004801421,0.0003768154,0.0001121211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.465311,0.00002785123,0.5342594,0.000009251185,0.00003717258,0.0002241617,0.000008887365,0.00008264142,0.00003963049],"genre_scores_gemma":[0.7729121,0.00001099687,0.2269767,0.000008068926,0.00003207379,0.00001225611,0.00002610335,0.00001495296,0.000006670148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3076012,"threshold_uncertainty_score":0.401053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01725327971111569,"score_gpt":0.2960128735713432,"score_spread":0.2787595938602275,"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."}}