{"id":"W4283711060","doi":"10.1038/s41598-022-21987-7","title":"Motion estimation for large displacements and deformations","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Computer science; Estimation; Motion (physics); Artificial intelligence; Geodesy; Geology; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009822106,0.00004656813,0.00004818575,0.0001140922,0.001243844,0.0002688319,0.0001113773,0.000005972312,0.00002120393],"category_scores_gemma":[0.00006884433,0.00004558239,0.0000227203,0.0002886645,0.00001730509,0.0008600661,0.0002443106,0.00003933948,0.000002570992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004087831,"about_ca_system_score_gemma":0.00002851036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.295625e-7,"about_ca_topic_score_gemma":6.107038e-7,"domain_scores_codex":[0.9990457,0.00001817846,0.0002012447,0.000317682,0.0002679329,0.0001492291],"domain_scores_gemma":[0.9994035,0.00001468637,0.0001411467,0.0003439227,0.00005299009,0.00004372985],"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":[0.00001019316,0.0004308431,0.004724606,0.00008691366,0.0000179373,0.00005251687,0.005957658,0.02068648,0.007601478,0.08177552,0.05211347,0.8265424],"study_design_scores_gemma":[0.0001284707,0.00001539087,0.0003374427,0.000003351615,0.000002099868,0.00009102542,0.00008830488,0.8854048,0.0005939287,0.03257862,0.0806889,0.00006769566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008579365,0.00002951695,0.9877856,0.0003330296,0.002772451,0.0002434665,0.000003073483,0.00006628426,0.0001872626],"genre_scores_gemma":[0.8977067,4.251375e-7,0.1004518,0.00009463233,0.000007693992,0.0000926355,0.00006854795,0.000003674599,0.001573902],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8891274,"threshold_uncertainty_score":0.9566771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266041490012542,"score_gpt":0.2878844661841353,"score_spread":0.2752240512840098,"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."}}