{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007737963,0.001045421,0.0002976033,0.00110835,0.0001368585,0.002156596,0.0009790667,0.0006816324,0.01678045],"category_scores_gemma":[0.007450213,0.0002325651,0.0004808105,0.001107623,0.000486144,0.00386818,0.000984701,0.001116181,0.002862428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004182126,"about_ca_system_score_gemma":0.0003205729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004443674,"about_ca_topic_score_gemma":0.0005572857,"domain_scores_codex":[0.9996314,0.0001564585,0.00001795025,0.0000698994,0.0001045185,0.00001967734],"domain_scores_gemma":[0.9956288,0.003500322,0.0002055542,0.0003854721,0.0002119253,0.00006781314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001506433,0.0002691087,0.001844921,0.0009517013,0.00003577738,0.0001742853,0.003072342,0.008161644,0.0283107,0.02295087,0.01873655,0.9153414],"study_design_scores_gemma":[0.0003189862,0.001915961,0.02383504,0.001377523,0.0003470206,0.002559725,0.003798313,0.1088473,0.07565531,0.1395576,0.6415204,0.0002668119],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2226333,0.008497502,0.6109631,0.003523882,0.0004514896,0.0003867158,0.0007559658,0.01268614,0.1401019],"genre_scores_gemma":[0.3510602,0.008061513,0.6085919,0.0006056174,0.00015763,0.0002445116,0.001423174,0.0009476055,0.0289079],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01678045,"threshold_uncertainty_score":0.05613625,"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."}}