{"id":"W6944349971","doi":"10.18720/spbpu/3/2019/vr/vr19-3404","title":"ЧиÑленное иÑÑледование пÑоÑеÑÑов нелинейного деÑоÑмиÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¶ÐµÐ»ÐµÐ·Ð¾Ð±ÐµÑоннÑÑ ÐºÐ¾Ð½ÑÑÑÑкÑий на оÑнове аналиÑиÑеÑÐºÐ¸Ñ ÑеÑений","year":2019,"lang":"ja","type":"article","venue":"Electronic Library of Peter the Great Polytechnic University","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Product (mathematics); Process (computing); Beam (structure)","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","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.002484953,0.004308116,0.004338026,0.002743984,0.001699034,0.0006807037,0.01180746,0.003641551,0.008605805],"category_scores_gemma":[0.0002472364,0.004277118,0.004788638,0.005106313,0.003004011,0.001274002,0.007548776,0.004988515,0.0009081855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00165351,"about_ca_system_score_gemma":0.003064229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008627497,"about_ca_topic_score_gemma":0.0003809208,"domain_scores_codex":[0.9785808,0.002623113,0.00338762,0.006187348,0.002830581,0.006390567],"domain_scores_gemma":[0.9833321,0.0007082468,0.003238552,0.01041181,0.0007667214,0.001542589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.008311248,0.004556207,0.02173879,0.00174227,0.01188859,0.0009807564,0.002593378,0.0002064473,0.7430955,0.01095564,0.1701328,0.02379841],"study_design_scores_gemma":[0.00653857,0.00449294,0.003808487,0.0006770949,0.003223884,0.000578991,0.001259478,0.0005794079,0.2032509,0.001239253,0.7692502,0.005100749],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8429015,0.0246956,0.00274801,0.00834749,0.001928206,0.008046807,0.00354578,0.002911934,0.1048746],"genre_scores_gemma":[0.8494316,0.01141341,0.001157875,0.002174125,0.001488855,0.00007347802,0.003087493,0.0008719561,0.1303012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5991175,"threshold_uncertainty_score":0.9998697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003167632300031858,"score_gpt":0.1800785955651736,"score_spread":0.1769109632651417,"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."}}