{"id":"W2033991989","doi":"10.1007/s10758-006-0001-z","title":"Characterizing Interaction with Visual Mathematical Representations","year":2006,"lang":"en","type":"article","venue":"International Journal of Computers for Mathematical Learning","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Visualization; Set (abstract data type); Representation (politics); Human–computer interaction; Context (archaeology); Selection (genetic algorithm); Visual reasoning; ENCODE; Cognition; Artificial intelligence","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":[0.00105239,0.0005705269,0.0004302402,0.001312821,0.0005611179,0.003139533,0.0006846517,0.001141153,0.008912495],"category_scores_gemma":[0.02008391,0.0003510856,0.0004696214,0.0009662957,0.0007359771,0.00410293,0.002443058,0.0008709768,0.0009253157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004433,"about_ca_system_score_gemma":0.0002483283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005962043,"about_ca_topic_score_gemma":0.0003927051,"domain_scores_codex":[0.9984321,0.0005667674,0.0000564283,0.0002414903,0.0004973788,0.0002058649],"domain_scores_gemma":[0.991118,0.005818452,0.0008563895,0.0008847285,0.0009633045,0.0003590993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005116156,0.0007768725,0.04641736,0.001011674,0.0002844354,0.001427341,0.01520592,0.07344166,0.2533201,0.2375461,0.01159675,0.3538556],"study_design_scores_gemma":[0.0001267791,0.000825431,0.0302691,0.00009688345,0.000150161,0.0008846431,0.004697484,0.7554256,0.03156013,0.1631381,0.01269454,0.000131172],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5575537,0.0002982861,0.4212955,0.0004348253,0.00006234825,0.00009320415,0.0003690351,0.001542771,0.01835026],"genre_scores_gemma":[0.9755406,0.0000897367,0.0221044,0.00005389203,0.00001869666,0.00006546907,0.0002603928,0.0002022641,0.001664669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008912495,"threshold_uncertainty_score":0.02981532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189776168736553,"score_gpt":0.3373142329662818,"score_spread":0.3183366160926265,"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."}}