{"id":"W4200497842","doi":"10.1002/essoar.10509050.1","title":"Pair Selection Optimization for InSAR Time Series Processing","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Geodynamics; Computer science; Interferometric synthetic aperture radar; Series (stratigraphy); World Wide Web; Library science; Geology; Artificial intelligence; Synthetic aperture radar; Seismology","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":[0.0001722956,0.0001551853,0.0002104342,0.00007066956,0.0002792973,0.0002461382,0.0002606882,0.0002031913,0.00003799204],"category_scores_gemma":[0.00006397072,0.0001474934,0.00007126586,0.0001759951,0.00003478017,0.0004082442,0.000468989,0.0001620039,0.000006728885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002536766,"about_ca_system_score_gemma":0.0002054862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001277839,"about_ca_topic_score_gemma":0.000009217317,"domain_scores_codex":[0.9990447,0.00004694896,0.0001710255,0.0004667142,0.00009137022,0.0001793073],"domain_scores_gemma":[0.9993355,0.00003528258,0.0001013401,0.0001993645,0.0003048973,0.00002359533],"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":[0.00004870174,0.0001805728,0.0009354479,0.0008653915,0.0002957173,0.000009576824,0.005002044,0.8376599,0.0001021259,0.004172196,0.01303463,0.1376937],"study_design_scores_gemma":[0.0001277218,0.00006435789,0.0004888661,0.00006407218,0.00001827085,0.00001865862,0.00006015727,0.9931543,0.001406149,0.001991453,0.002352611,0.000253421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002216489,0.0003543943,0.9929814,0.002270761,0.000756856,0.000240804,0.000001194688,0.0003873832,0.0007907177],"genre_scores_gemma":[0.05534239,0.00004594477,0.9361938,0.0006259747,0.0001899285,0.0001443381,0.00006313471,0.00001408243,0.007380372],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1554944,"threshold_uncertainty_score":0.6014607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01635061599885774,"score_gpt":0.239574373959165,"score_spread":0.2232237579603072,"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."}}