{"id":"W1985548644","doi":"10.3758/brm.40.3.858","title":"Recovering data from scanned graphs: Performance of Frantz’s g3data software","year":2008,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"Natural Sciences and Engineering Research Council of Canada; Trent University","keywords":"Computer science; Software; Computer graphics (images); Programming language","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":[0.004661172,0.002909199,0.001399768,0.00597395,0.001282478,0.003048039,0.004367245,0.001238661,0.02475776],"category_scores_gemma":[0.02613699,0.001042818,0.001080737,0.004254367,0.0009504718,0.002825774,0.002439244,0.001844236,0.009498064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001252209,"about_ca_system_score_gemma":0.002002352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01515052,"about_ca_topic_score_gemma":0.007961531,"domain_scores_codex":[0.9964569,0.0004651033,0.0003272769,0.0008221129,0.001709277,0.0002193716],"domain_scores_gemma":[0.9857461,0.008587775,0.0005676819,0.002707983,0.002004639,0.0003858175],"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":[0.002770827,0.0008029708,0.01120713,0.0009300862,0.0004702858,0.001232101,0.00247794,0.03804151,0.0347493,0.009285166,0.1191098,0.7789229],"study_design_scores_gemma":[0.0006448149,0.0004644106,0.01499021,0.0002248371,0.0002011421,0.0009015989,0.001270185,0.7373783,0.1575287,0.02101579,0.06488273,0.000497247],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.06837386,0.0003821863,0.3582982,0.0006482702,0.0002712708,0.0003692433,0.004704084,0.5569757,0.009977165],"genre_scores_gemma":[0.3071704,0.0005522896,0.621751,0.0003514392,0.00006852447,0.0006417535,0.008754769,0.05367784,0.007032049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02475776,"threshold_uncertainty_score":0.08282298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5383671611823693,"score_gpt":0.5695334473161054,"score_spread":0.03116628613373618,"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."}}