{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00235531,0.0003640003,0.0004199317,0.0004930405,0.0007760559,0.0004872373,0.001485366,0.0002234365,0.0008517794],"category_scores_gemma":[0.001222957,0.0003619424,0.0003173371,0.002728919,0.0003053334,0.002069444,0.001131336,0.001215583,0.001880855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002084488,"about_ca_system_score_gemma":0.0006804251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006320927,"about_ca_topic_score_gemma":0.0004316766,"domain_scores_codex":[0.9933652,0.0009235822,0.0006059255,0.001108869,0.002352248,0.001644171],"domain_scores_gemma":[0.99567,0.0006503896,0.0001772937,0.001073576,0.001649693,0.0007791005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006180658,0.001684118,0.2984657,0.0001224878,0.0005984171,0.007123498,0.005182451,0.0000486902,0.008923502,0.2634473,0.4131638,0.0006219702],"study_design_scores_gemma":[0.02830535,0.003048135,0.2181185,0.001785195,0.0003085981,0.006018077,0.06710134,0.04358878,0.1613926,0.04756311,0.4145595,0.00821082],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7406028,0.0001840398,0.02057948,0.002865716,0.002178847,0.001625529,0.0001849538,0.0002340384,0.2315446],"genre_scores_gemma":[0.9915566,0.00004005263,0.002107308,0.0004805185,0.0002747776,0.000116961,0.00005171854,0.00005241088,0.005319613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2509539,"threshold_uncertainty_score":0.9998832,"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."}}