{"id":"W7135430583","doi":"","title":"Visualising Magnitude:Graphical Number Representations Help Users Detect Large Number Entry Errors","year":2015,"lang":"en","type":"article","venue":"Research Portal (King's College London)","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Population and Public Health","funders":"","keywords":"Representation (politics); Graphical display; Graphical user interface; Data entry; Work (physics); Visualization; Entry Level","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":[0.001406622,0.001317784,0.0004663604,0.0008850565,0.0002230484,0.001604568,0.0008823146,0.001048664,0.02377683],"category_scores_gemma":[0.02202304,0.0003598497,0.0004574714,0.0003828495,0.000457893,0.002792848,0.00199197,0.0007361643,0.002891171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002293663,"about_ca_system_score_gemma":0.0002685031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005484388,"about_ca_topic_score_gemma":0.0006027289,"domain_scores_codex":[0.9991516,0.0004216541,0.00005749107,0.0001206588,0.0001816342,0.00006688367],"domain_scores_gemma":[0.9891902,0.008199234,0.0008669972,0.0006634983,0.0007859657,0.0002941724],"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":[0.006095261,0.0008823609,0.01968896,0.003546212,0.0001543803,0.001207072,0.01573218,0.005662852,0.1966629,0.00940508,0.03825,0.7027128],"study_design_scores_gemma":[0.002648325,0.01298316,0.2107177,0.005008136,0.001187482,0.007123851,0.01556033,0.1536133,0.2283474,0.03488983,0.3262732,0.001647382],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5454589,0.001389233,0.3919354,0.002527545,0.0007278306,0.0008535299,0.002111699,0.0228379,0.03215804],"genre_scores_gemma":[0.6945578,0.0009489546,0.2929962,0.0006989315,0.0001701924,0.0005451872,0.0009546932,0.001134708,0.007993191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02377683,"threshold_uncertainty_score":0.07954144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05574134790828656,"score_gpt":0.4000332426172505,"score_spread":0.3442918947089639,"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."}}