{"id":"W2974183472","doi":"10.1167/19.10.288c","title":"Fast motion drags shape","year":2019,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Motion (physics); Computer science; Geology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001316772,0.000432446,0.0003301493,0.0003897579,0.0004237937,0.0006281614,0.0002243881,0.00036286,0.006353577],"category_scores_gemma":[0.001743248,0.0003436605,0.0003373631,0.000177339,0.000332065,0.0007732607,0.00135306,0.0006849925,0.0006158794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004646331,"about_ca_system_score_gemma":0.0003390032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001666905,"about_ca_topic_score_gemma":0.001614137,"domain_scores_codex":[0.9998449,0.000007945723,0.000007775961,0.00003242391,0.00007863266,0.00002834152],"domain_scores_gemma":[0.9993145,0.0002016496,0.00009697711,0.0001905282,0.00007488859,0.0001214264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008199512,0.00005455569,0.003596027,0.0001530818,0.00001427218,0.0003161124,0.0002687682,0.001297088,0.926789,0.00275549,0.001411202,0.0625245],"study_design_scores_gemma":[0.0003876874,0.005100842,0.480638,0.0001707206,0.0002289477,0.004021204,0.0009509795,0.03348871,0.41808,0.009812872,0.04692276,0.0001971856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851661,0.0004008446,0.006066679,0.0001650141,0.0001230454,0.00004759664,0.0001125004,0.0003767886,0.007541377],"genre_scores_gemma":[0.9864116,0.000440489,0.006384994,0.0002783507,0.00004174968,0.00002929998,0.0002368447,0.0002271217,0.005949542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006353577,"threshold_uncertainty_score":0.02125484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01190602008989271,"score_gpt":0.2987715230785424,"score_spread":0.2868655029886497,"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."}}