{"id":"W4393493909","doi":"10.5281/zenodo.10572207","title":"Time-series displacement from high-resolution satellite Synthetic Aperture Radar (SAR) data using Sub-Pixel Offset Tracking (SPOT)","year":2020,"lang":"en","type":"dataset","venue":"Explore Bristol Research","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Synthetic aperture radar; Remote sensing; Offset (computer science); Tracking (education); Satellite; Side looking airborne radar; Pixel; Displacement (psychology); Inverse synthetic aperture radar; High resolution; Geodesy; Radar imaging; Geology; Computer science; Radar; Computer vision; Bistatic radar; Physics; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005949629,0.0006269084,0.0004020918,0.001941131,0.0002417993,0.0007504749,0.0007291221,0.0006566237,0.005601036],"category_scores_gemma":[0.001398779,0.0002548268,0.0009694116,0.002692638,0.000169975,0.0008024544,0.0006397606,0.0006151939,0.004962158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003472165,"about_ca_system_score_gemma":0.0006214055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02238358,"about_ca_topic_score_gemma":0.03350303,"domain_scores_codex":[0.9996566,0.00002870977,0.00004112452,0.000123654,0.0001091874,0.00004076863],"domain_scores_gemma":[0.9993246,0.00008843953,0.0001165932,0.0001358979,0.0002700812,0.00006448297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001765162,0.001261103,0.2978033,0.002260721,0.001272495,0.001364139,0.0008229406,0.08451055,0.02127719,0.001688033,0.3451087,0.2408656],"study_design_scores_gemma":[0.0003429157,0.0002915649,0.8018012,0.0002013985,0.0002493558,0.0005652003,0.0007050018,0.09553263,0.008242078,0.0009350851,0.09092405,0.0002094582],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3421937,0.0004578462,0.006691134,0.000316759,0.0003843535,0.000147421,0.6401221,0.003357755,0.006328994],"genre_scores_gemma":[0.2194524,0.0002556171,0.01298475,0.00004878295,0.0001184903,0.0001581961,0.7633251,0.0003232382,0.00333334],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02238358,"threshold_uncertainty_score":0.04450655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1256781529214549,"score_gpt":0.3312251320156899,"score_spread":0.205546979094235,"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."}}