{"id":"W159272853","doi":"10.1007/978-1-4471-5195-1_29","title":"Detecting, Representing and Attending to Visual Shape","year":2013,"lang":"en","type":"book-chapter","venue":"Advances in computer vision and pattern recognition","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Optometry; Psychology; Computer graphics (images); Cartography; Geography; Medicine","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":[0.0001846928,0.0008019338,0.0005211075,0.000697794,0.0002259834,0.001667447,0.00145443,0.0009262454,0.009461398],"category_scores_gemma":[0.0005442143,0.0003334523,0.0004330451,0.0009712022,0.0006472712,0.001796732,0.0006449254,0.0006991499,0.004849849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004899015,"about_ca_system_score_gemma":0.000408936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001537052,"about_ca_topic_score_gemma":0.001946333,"domain_scores_codex":[0.9998825,0.000007195685,0.000003875168,0.00004520908,0.00004513266,0.00001600723],"domain_scores_gemma":[0.9998974,0.00004173999,0.00001090771,0.000014804,0.00002385449,0.00001121988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006704133,0.00003270897,0.0003432933,0.0005256881,0.00003958042,0.00006577304,0.0001831593,0.002033115,0.08308484,0.03251643,0.02439436,0.8567141],"study_design_scores_gemma":[0.00004578528,0.0002673684,0.009579766,0.0004189484,0.000182701,0.001582312,0.0004161086,0.1055205,0.1349594,0.2986493,0.4482481,0.0001296746],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.03243538,0.0737315,0.7078549,0.001929665,0.002091072,0.0001420743,0.0004622568,0.003971701,0.1773815],"genre_scores_gemma":[0.300081,0.06915541,0.388361,0.0009520913,0.001716361,0.0002433331,0.001553828,0.00103743,0.2368995],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.009461398,"threshold_uncertainty_score":0.03165156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02662685311385348,"score_gpt":0.309598741131459,"score_spread":0.2829718880176055,"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."}}