{"id":"W1986844135","doi":"10.1109/tip.2012.2221726","title":"Catching a Rat by Its Edglets","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Tracking (education); Frame (networking); Sliding window protocol; Window (computing); Robustness (evolution); Image processing; Boundary (topology); Pattern recognition (psychology); Image (mathematics); Mathematics","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":[0.0006091528,0.0001914837,0.0001752513,0.0001246227,0.000487559,0.0003405644,0.0004214971,0.00006962879,0.00001833386],"category_scores_gemma":[0.00001348867,0.0001823659,0.00008109432,0.0004825679,0.00003715072,0.002516203,0.000003082432,0.0003289566,0.0001186988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005285071,"about_ca_system_score_gemma":0.0000661626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008446283,"about_ca_topic_score_gemma":0.000002026492,"domain_scores_codex":[0.9985424,0.0001257716,0.0002218892,0.0003301544,0.0002742225,0.0005056146],"domain_scores_gemma":[0.9992393,0.0001160674,0.0000846444,0.0003111034,0.00008886629,0.0001600279],"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":[0.000009561571,0.000278915,0.00004387471,0.00008399463,0.00001635711,0.000005857273,0.001873571,0.0002063351,0.09648485,0.00004500904,0.0005050702,0.9004466],"study_design_scores_gemma":[0.0006423212,0.00006569352,0.0001548445,0.0001721649,0.00002850352,0.0001213633,0.00008436551,0.02727579,0.9644099,0.0003172414,0.006022681,0.0007051658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004200084,0.0009602965,0.991852,0.0005307469,0.0009633648,0.00009867399,0.000003450516,0.0003614666,0.001029894],"genre_scores_gemma":[0.885928,0.00002414529,0.1131248,0.0004240106,0.00008423308,0.00002687922,6.027816e-7,0.00002158617,0.0003657351],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8997415,"threshold_uncertainty_score":0.7436666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02500305423925454,"score_gpt":0.3041002851527391,"score_spread":0.2790972309134846,"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."}}