{"id":"W4302598849","doi":"10.48550/arxiv.1607.04673","title":"Unifying Registration based Tracking: A Case Study with Structural\\n Similarity","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"BitTorrent tracker; Computer science; Similarity (geometry); Tracking (education); Data mining; Decomposition; Similarity measure; Plug-in; Artificial intelligence; Measure (data warehouse); Image (mathematics); Computer vision; Information retrieval; Eye tracking","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":[0.01232347,0.00147512,0.001872901,0.003243054,0.001795811,0.004435793,0.003540315,0.004648193,0.003340512],"category_scores_gemma":[0.03155666,0.0008026058,0.001823299,0.005895947,0.002151087,0.00667571,0.005442397,0.002773933,0.002194577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001377365,"about_ca_system_score_gemma":0.001473402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006427573,"about_ca_topic_score_gemma":0.007424587,"domain_scores_codex":[0.9890404,0.003614443,0.0009035134,0.002411862,0.003480467,0.0005492793],"domain_scores_gemma":[0.9818655,0.008333539,0.001364721,0.006377577,0.001576213,0.0004824718],"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":[0.0007863966,0.0009518627,0.01264138,0.001047362,0.0003777869,0.001904572,0.002188803,0.1082386,0.03337229,0.06878062,0.008238298,0.761472],"study_design_scores_gemma":[0.0001377592,0.0009663406,0.006066084,0.0001813753,0.0002435777,0.004081609,0.000831796,0.8441505,0.0554041,0.05663751,0.03112786,0.0001715526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05583787,0.001653306,0.9306348,0.000525891,0.0001442996,0.0003838092,0.0002304112,0.00363059,0.006959049],"genre_scores_gemma":[0.3419529,0.0009261622,0.6504647,0.0001803174,0.0001113525,0.0001892378,0.0007318592,0.0009785548,0.004465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01232347,"threshold_uncertainty_score":0.06517351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1061362787233068,"score_gpt":0.2406312778772314,"score_spread":0.1344949991539246,"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."}}