{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001621534,0.0004708058,0.0002485349,0.0003872619,0.0001507109,0.0006989454,0.0005735286,0.000392443,0.001554442],"category_scores_gemma":[0.0023158,0.0001933894,0.0003098353,0.0004096344,0.0001678612,0.000593855,0.000411472,0.000296971,0.0002390834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003607377,"about_ca_system_score_gemma":0.0003523069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004778146,"about_ca_topic_score_gemma":0.004513402,"domain_scores_codex":[0.9997488,0.0001020885,0.00001896588,0.00003679237,0.00006196868,0.00003136783],"domain_scores_gemma":[0.9991044,0.0004081163,0.00005778673,0.0001469186,0.0002190198,0.00006374235],"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":[0.000490719,0.0001224962,0.01758245,0.0001192617,0.00007278752,0.0001008862,0.00004397143,0.9334507,0.01004468,0.001691011,0.000607257,0.03567376],"study_design_scores_gemma":[0.00001234598,0.00006583704,0.00123404,0.000004957269,0.000008650496,0.00001348698,0.00002030459,0.9957266,0.002272224,0.0002168223,0.0004192986,0.000005501648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8363862,0.0005261558,0.1447423,0.0003510776,0.0001102133,0.000132471,0.002015577,0.002412756,0.01332328],"genre_scores_gemma":[0.9548835,0.0001393491,0.04339864,0.00003489872,0.000006399644,0.00002712602,0.0008905733,0.00005926596,0.0005602743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004778146,"threshold_uncertainty_score":0.009500682,"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."}}