{"id":"W2092772700","doi":"10.1007/s10032-004-0120-9","title":"A survey of table recognition","year":2004,"lang":"en","type":"article","venue":"International Journal on Document Analysis and Recognition (IJDAR)","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":282,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Table (database); Computer science; Decision table; Feature (linguistics); Presentation (obstetrics); Data mining; Artificial intelligence; Machine learning","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":[0.0009475324,0.0001279919,0.0002276496,0.0007844839,0.0001040542,0.0003938553,0.0004278944,0.00006178195,0.0002626562],"category_scores_gemma":[0.0001345045,0.0001050916,0.0001714424,0.0009054731,0.00004685831,0.000705357,0.00006789594,0.0001775697,0.00004229663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001107954,"about_ca_system_score_gemma":0.00007872116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000238434,"about_ca_topic_score_gemma":0.00003807862,"domain_scores_codex":[0.9982841,0.0001225086,0.0005382645,0.0002532766,0.0006662561,0.0001356171],"domain_scores_gemma":[0.9979508,0.00008920679,0.0004839656,0.0001459502,0.001227687,0.0001024101],"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":[0.0001926753,0.0007495169,0.006094517,0.00001249773,0.00268201,0.0000548949,0.000327774,0.00008615047,0.002691629,0.003435523,0.0003249549,0.9833478],"study_design_scores_gemma":[0.005968244,0.001565753,0.3218808,0.000658408,0.001049673,0.0003671465,0.0001778941,0.00456313,0.3749903,0.2839038,0.00339264,0.001482213],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1625601,0.0001728805,0.8330472,0.002162812,0.0004880828,0.000130959,0.00007189776,0.00007271497,0.001293397],"genre_scores_gemma":[0.9900427,0.00122073,0.007988664,0.0003499113,0.0000811511,0.000007209939,0.000140637,0.000005916825,0.0001630581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9818656,"threshold_uncertainty_score":0.428551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0307860651868092,"score_gpt":0.2944813659463421,"score_spread":0.2636953007595328,"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."}}