{"id":"W2335570229","doi":"10.1177/154193120204601708","title":"Judgments of 3D Bars in Depth","year":2002,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Scaling; Bar (unit); Statistics; Mathematics; Approximation error; Geometry; Apparent Size; Physics; Psychology; Cognitive 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":[],"consensus_categories":[],"category_scores_codex":[0.0002509683,0.0001035538,0.0001751505,0.00003582692,0.0001269901,0.00005952023,0.000527705,0.00005203166,0.000003496681],"category_scores_gemma":[0.00004556118,0.00008086763,0.00008324777,0.0002100107,0.00008671587,0.0004219486,0.0003867172,0.00009676985,3.707221e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002767298,"about_ca_system_score_gemma":0.000005914793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003759504,"about_ca_topic_score_gemma":0.000004037177,"domain_scores_codex":[0.9992142,0.000004544787,0.0003086748,0.0001880948,0.0001262935,0.0001581819],"domain_scores_gemma":[0.9994975,0.00002621914,0.0002700084,0.00008562896,0.00008256915,0.00003809717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005745074,0.0003321371,0.8560982,0.0004041092,0.0001018461,1.071515e-7,0.06288964,0.0002256902,0.008285736,0.06294104,0.006456792,0.002258916],"study_design_scores_gemma":[0.00370646,0.0004419925,0.4998675,0.001525393,0.0001304047,0.00000629371,0.03918071,0.3693449,0.07237083,0.005785388,0.005778641,0.001861486],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99819,0.00005270568,0.0001069239,0.00005395203,0.00005106768,0.00006319894,0.000009420745,0.00001558638,0.001457116],"genre_scores_gemma":[0.9970316,0.00008772552,0.002682559,0.0000787531,0.00001481716,8.763656e-7,8.698582e-7,0.000005604684,0.00009718277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3691193,"threshold_uncertainty_score":0.3297686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02820221458998811,"score_gpt":0.2512582793017291,"score_spread":0.223056064711741,"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."}}