{"id":"W7100095312","doi":"","title":"2011 Canadian Conference on Computer and Robot Vision Object Detection Using Principal Contour Fragments","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Object (grammar); Edge detection; Invariant (physics); Pixel; Pattern recognition (psychology); Mutual information; Object detection; Similarity (geometry); Cognitive neuroscience of visual object recognition; Relation (database)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001032448,0.0001372109,0.0001296815,0.0001475467,0.0001696733,0.0002687247,0.000253371,0.0000718463,0.00008020128],"category_scores_gemma":[0.000009496402,0.0001178246,0.00002438182,0.0001029882,0.00003380166,0.0009493975,0.0001305885,0.0001272365,0.00008013334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001023909,"about_ca_system_score_gemma":0.00006490073,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04205343,"about_ca_topic_score_gemma":0.005742789,"domain_scores_codex":[0.9990165,0.00003990921,0.0001524432,0.0003511799,0.0001554286,0.0002845262],"domain_scores_gemma":[0.9992914,0.00002820339,0.00005335514,0.0002811195,0.0001279142,0.0002180326],"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":[0.000009517835,0.00005141794,0.001140371,0.0000152148,0.00001783787,0.00002341124,0.0002089298,0.0001018702,0.02836566,0.006257811,0.0005154511,0.9632925],"study_design_scores_gemma":[0.0005343222,0.0009249159,0.0409864,0.00009695139,0.000006064398,0.00005092889,0.00001962664,0.820491,0.1270931,0.007592189,0.001638665,0.0005658829],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0651252,0.00002036316,0.9326261,0.0001780386,0.0001459651,0.0002998208,6.3082e-7,0.0001548664,0.001449039],"genre_scores_gemma":[0.8649393,0.00001937966,0.1341861,0.0006423505,0.00003941726,0.000007064302,5.718035e-7,0.000006675646,0.0001591218],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9627267,"threshold_uncertainty_score":0.9643256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02851129907251576,"score_gpt":0.2842130586609524,"score_spread":0.2557017595884367,"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."}}