{"id":"W4393487671","doi":"10.5281/zenodo.10572206","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":"Zenodo (CERN European Organization for Nuclear 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); Satellite; Side looking airborne radar; Pixel; Displacement (psychology); Tracking (education); Geodesy; Inverse synthetic aperture radar; Series (stratigraphy); Interferometric synthetic aperture radar; Geology; Radar imaging; Radar; Computer science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004273143,0.0005399047,0.0004853832,0.0002848321,0.001258678,0.001138312,0.002470638,0.0002898356,0.003130085],"category_scores_gemma":[0.000799053,0.0006140706,0.00008271263,0.000587736,0.0001884129,0.0007854851,0.002750381,0.0008722464,0.0126049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006248886,"about_ca_system_score_gemma":0.00001163387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007079047,"about_ca_topic_score_gemma":0.00000152758,"domain_scores_codex":[0.9966996,0.0003617031,0.0005584669,0.0009712706,0.0007725415,0.0006364744],"domain_scores_gemma":[0.9975808,0.0001066657,0.0001893324,0.001598345,0.0002226955,0.0003022237],"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":[0.00008225838,0.00005380482,3.117439e-7,0.0003347165,0.0002079823,0.00006371302,0.0001216915,0.0006377408,0.07764399,0.00001007601,0.912015,0.008828727],"study_design_scores_gemma":[0.0003472208,0.00009158993,0.00001223591,0.0003769896,0.0001438643,0.00004496419,0.00008081443,0.002338693,0.002330165,0.0000187233,0.9936155,0.0005992124],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006624695,0.0009859395,0.004847423,0.0001296816,0.0005570493,0.0006354048,0.9907963,0.001092554,0.0002931634],"genre_scores_gemma":[0.002327594,0.001738738,0.001006166,0.00004775285,0.0009103736,5.303754e-8,0.9917898,0.002148835,0.00003072448],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08160054,"threshold_uncertainty_score":0.9998986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03843394255687681,"score_gpt":0.2341406599543338,"score_spread":0.195706717397457,"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."}}