{"id":"W4366549398","doi":"10.1145/3544548.3581113","title":"ChartDetective: Easy and Accurate Interactive Data Extraction from Complex Vector Charts","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Agence Nationale de la Recherche","keywords":"Computer science; Raster data; Data mining; Raster graphics; Data extraction; Interface (matter); Artificial intelligence; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001532419,0.001738813,0.0007100272,0.002462494,0.0004243276,0.001920463,0.001556646,0.0006092158,0.02166859],"category_scores_gemma":[0.009373979,0.0005038257,0.0005680704,0.00158188,0.0005575061,0.002269307,0.00270705,0.0008394711,0.005575154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002843395,"about_ca_system_score_gemma":0.0009862657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002173096,"about_ca_topic_score_gemma":0.00311225,"domain_scores_codex":[0.9990413,0.0001715792,0.00008150841,0.0001878891,0.0004396259,0.00007824516],"domain_scores_gemma":[0.9956585,0.002337462,0.000273255,0.0007851861,0.000768363,0.0001772096],"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":[0.0009362844,0.0001887329,0.004605761,0.001538701,0.0001081058,0.0005998695,0.001837383,0.003799949,0.07800803,0.00665396,0.183323,0.7184003],"study_design_scores_gemma":[0.0003450835,0.0005472988,0.02464258,0.0005822665,0.000110763,0.001868431,0.001653213,0.2478604,0.3075827,0.02133613,0.3927769,0.0006942411],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01499125,0.0003067616,0.7230562,0.0002624626,0.0001459946,0.0002786363,0.00692537,0.2509984,0.003035004],"genre_scores_gemma":[0.1339966,0.0005826004,0.8272195,0.0002580973,0.00009726337,0.0007638069,0.0120533,0.01807585,0.006953069],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02166859,"threshold_uncertainty_score":0.07248861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1290413302430225,"score_gpt":0.3909459923106922,"score_spread":0.2619046620676697,"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."}}