{"id":"W2025730803","doi":"10.1016/j.patcog.2009.03.028","title":"Detection of unexpected multi-part objects from segmented contour maps","year":2009,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001228473,0.0001464758,0.0001763926,0.0001941252,0.000076859,0.0000662036,0.0002142764,0.00009959767,0.0000424784],"category_scores_gemma":[0.00004258627,0.0001519695,0.00008604587,0.0003079839,0.00001652434,0.0004743832,0.00003277614,0.0001359648,0.00008458165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005655959,"about_ca_system_score_gemma":0.00001500031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003434501,"about_ca_topic_score_gemma":0.0001523828,"domain_scores_codex":[0.9988548,0.0001107011,0.0003122683,0.0003328177,0.0002000199,0.0001894071],"domain_scores_gemma":[0.9992116,0.00004163029,0.0002021764,0.0002930063,0.0001985218,0.00005305773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001116741,0.00009407689,0.00003989921,0.000004896696,0.00001146528,0.000006510899,0.0002904326,2.646956e-7,0.2978724,4.531591e-7,0.00007466851,0.7015938],"study_design_scores_gemma":[0.0005379453,0.0002556324,0.005630606,0.00007463601,0.00001274546,0.000008880674,0.00005327578,0.002634461,0.9885314,0.002013616,0.00006624443,0.0001805551],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1470713,0.0000496186,0.8512456,0.00006151017,0.0003454254,0.0002398581,0.00003261941,0.0005664235,0.0003876064],"genre_scores_gemma":[0.9901984,0.00002699923,0.009071971,0.00045803,0.0001129597,0.00002569187,0.00006565297,0.000008483456,0.00003182947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8431271,"threshold_uncertainty_score":0.6197135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02194818887378805,"score_gpt":0.2406833321509718,"score_spread":0.2187351432771837,"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."}}