{"id":"W3035736794","doi":"10.1049/el.2020.1362","title":"Towards video based collective motion analysis through shape tracking and matching","year":2020,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tracking (education); Computer vision; Motion analysis; Artificial intelligence; Matching (statistics); Computer science; Motion (physics); Match moving; Video tracking; Video processing; Mathematics; Psychology","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":[0.0001323877,0.000143868,0.0002025766,0.000105857,0.0001984706,0.0002164568,0.0003082012,0.00002670163,0.00001515267],"category_scores_gemma":[0.000041958,0.0001459602,0.00009779075,0.001322148,0.00002555264,0.0007828359,0.00008685907,0.0002537171,0.000004800274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001487601,"about_ca_system_score_gemma":0.00007483555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009529172,"about_ca_topic_score_gemma":0.000004014587,"domain_scores_codex":[0.9987084,0.00006961032,0.0001680865,0.0004745823,0.0002344281,0.0003449139],"domain_scores_gemma":[0.9995489,0.00006405305,0.00008478863,0.0001879335,0.00003605456,0.00007824929],"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":[0.0001015746,0.0001183874,0.002275243,0.00008057366,0.0008851787,0.00009779874,0.0229618,0.07598048,0.2904172,0.01943919,0.002246159,0.5853964],"study_design_scores_gemma":[0.0003715107,0.00005978794,0.001284055,0.000008014578,0.0000576177,0.000002773901,0.00003658964,0.9843687,0.01025873,0.00204452,0.001291073,0.0002166074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02983391,0.0002737941,0.9355898,0.0338686,0.000036134,0.0000906776,8.744071e-7,0.0001506963,0.0001554995],"genre_scores_gemma":[0.87119,0.00002024547,0.09949567,0.02923039,0.00003936957,0.000005256223,0.000003413584,0.00001116968,0.000004551161],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9083883,"threshold_uncertainty_score":0.5952086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01711331175577895,"score_gpt":0.2614641688420991,"score_spread":0.2443508570863201,"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."}}