{"id":"W206788155","doi":"10.4018/978-1-60566-904-5.ch015","title":"Using Graphics to Improve Understanding of Conceptual Models","year":2010,"lang":"en","type":"book-chapter","venue":"Advances in database research (ADR) book series/Advances in database research series","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Graphics; Cognitive load; Computer science; Comprehension; Cognition; Domain (mathematical analysis); Human–computer interaction; Computer graphics; Multimedia; Artificial intelligence; Computer graphics (images); Programming language; Psychology","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":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","sts","open_science"],"category_scores_codex":[0.02290685,0.001850208,0.00341936,0.009508311,0.001320677,0.0006057193,0.00958489,0.0009805689,0.000362229],"category_scores_gemma":[0.005945561,0.001882313,0.0005155292,0.005193987,0.0125403,0.05727638,0.01482034,0.01066011,0.00006209451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002909185,"about_ca_system_score_gemma":0.002671465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004860913,"about_ca_topic_score_gemma":0.01334915,"domain_scores_codex":[0.973793,0.002249825,0.004370651,0.005220634,0.009910887,0.004454944],"domain_scores_gemma":[0.9785182,0.005259793,0.001376804,0.01023344,0.003390286,0.001221406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008507558,0.0002292952,0.00003492443,0.001148564,0.00008441931,0.001137278,0.0007602366,0.001247415,0.008902994,0.9809061,0.0004490806,0.004248928],"study_design_scores_gemma":[0.0008736091,0.0009750903,0.000001006936,0.003937058,0.00002240815,0.00008051613,0.001901929,0.003413706,0.0227094,0.4493198,0.5149832,0.001782263],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002263862,0.1090362,0.7111667,0.001476782,0.001443441,0.01038159,0.0130688,0.0007097179,0.1524904],"genre_scores_gemma":[0.003612762,0.4782653,0.4946766,0.0001980888,0.0006695579,0.001222179,0.00219837,0.0006032609,0.0185539],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5315862,"threshold_uncertainty_score":0.9999795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2022430374626522,"score_gpt":0.4459812781824064,"score_spread":0.2437382407197543,"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."}}