{"id":"W4392657519","doi":"10.5194/egusphere-egu24-18388","title":"Intra-annual velocity variability extracted from multi-sensor and multi-temporal datasets produced by different processing chains","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Ottawa","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008035709,0.0007159581,0.0009313,0.0001373041,0.0002640279,0.001275305,0.001192268,0.0004091627,0.00006202517],"category_scores_gemma":[0.0002912546,0.0005584012,0.0001902566,0.0003198307,0.0001572324,0.0004048841,0.005211285,0.001316154,0.00001368596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001392433,"about_ca_system_score_gemma":0.0001985924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001826904,"about_ca_topic_score_gemma":0.0003369289,"domain_scores_codex":[0.9951252,0.0002717678,0.0009336467,0.002620969,0.000487159,0.0005613015],"domain_scores_gemma":[0.9973986,0.0001398401,0.0004435733,0.001505317,0.0002128532,0.0002998907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001625489,0.005259057,0.0213483,0.003995488,0.002434504,0.0002582826,0.0190129,0.0003465313,0.03426199,0.001545536,0.006825091,0.9045498],"study_design_scores_gemma":[0.0003975803,0.00003093771,0.009046196,0.0001928318,0.0001568253,0.000004541765,0.0001283443,0.9859824,0.002241914,0.0004726304,0.0005406602,0.000805156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3027524,0.0007304968,0.6887011,0.0009194794,0.0005815841,0.0007556105,0.004915277,0.0006192703,0.00002479952],"genre_scores_gemma":[0.7772592,0.00002087308,0.2198021,0.00008022772,0.000193913,0.00005790855,0.002138831,0.00004172961,0.0004052035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9856359,"threshold_uncertainty_score":0.9997615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02993874776868875,"score_gpt":0.2728265550699406,"score_spread":0.2428878073012519,"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."}}