{"id":"W2145816546","doi":"10.1109/icpr.1992.201518","title":"Attribute grammar for shape recognition and its VLSI implementation","year":2003,"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":"Concordia University","funders":"","keywords":"Computer science; Very-large-scale integration; String (physics); Pattern recognition (psychology); Architecture; Grammar; Artificial intelligence; Algorithm; Theoretical computer science; Natural language processing; 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":[0.0002011809,0.00006826537,0.00006721201,0.00004342382,0.0000826158,0.00006780004,0.00009189575,0.00002581146,0.00003901709],"category_scores_gemma":[0.0000668052,0.00006214437,0.00002370112,0.000143387,0.000006561329,0.0007327655,0.00003004563,0.00002986797,0.00001073878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001509201,"about_ca_system_score_gemma":0.00001429296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002158115,"about_ca_topic_score_gemma":0.000002697929,"domain_scores_codex":[0.9994294,0.00002071309,0.0001217124,0.0002041926,0.00007142002,0.0001525849],"domain_scores_gemma":[0.9996546,0.00005584012,0.0000414758,0.00009443332,0.0001115698,0.0000421074],"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":[0.000004460039,0.00001914638,0.0001365668,0.00002333649,0.000006994969,0.000001364447,0.00007465187,1.101945e-7,0.00854704,0.04908741,0.001439976,0.9406589],"study_design_scores_gemma":[0.0005278098,0.0002580051,0.0002377925,0.000007147975,0.000008256701,0.00001415374,0.00004754982,0.001641287,0.8629238,0.1007226,0.03342308,0.0001884661],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007392677,0.0001262428,0.9913562,0.0001963845,0.0000447752,0.0003606825,0.000009367879,0.000181044,0.0003326365],"genre_scores_gemma":[0.3492275,0.0003200969,0.6489448,0.000961608,0.00003560369,0.0001216744,0.00004604814,0.00001195641,0.0003306609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9404705,"threshold_uncertainty_score":0.2534174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07773693621228263,"score_gpt":0.3459214437436185,"score_spread":0.2681845075313359,"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."}}