{"id":"W2921869280","doi":"10.1145/3294109.3295627","title":"You say Potato, I say Po-Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Visualization; Computer science; Leverage (statistics); Annotation; Human–computer interaction; Data visualization; Block (permutation group theory); Information visualization; Fidelity; Usability; World Wide Web; Computer graphics (images); Multimedia; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001952402,0.00054373,0.0004089056,0.0009899928,0.0009199668,0.003612414,0.0007598053,0.0009611204,0.1125817],"category_scores_gemma":[0.01683194,0.0003284508,0.0004745253,0.001607536,0.0008258543,0.006055564,0.002136658,0.001664971,0.04237408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004751853,"about_ca_system_score_gemma":0.0005943038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001155364,"about_ca_topic_score_gemma":0.001979852,"domain_scores_codex":[0.9990914,0.0003480038,0.00005611405,0.000180253,0.000262158,0.00006208931],"domain_scores_gemma":[0.9934793,0.002814312,0.0003650682,0.001635411,0.001228553,0.0004774387],"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":[0.0004543699,0.00007098701,0.00694727,0.0007607892,0.00004971682,0.0004642329,0.006501588,0.0005950827,0.004930768,0.02540499,0.7878136,0.1660066],"study_design_scores_gemma":[0.00002855177,0.0000483226,0.002349667,0.0002016116,0.00002023288,0.0004547381,0.003026139,0.001419301,0.002871517,0.01271956,0.9768122,0.00004815845],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.05675391,0.002950453,0.2561305,0.06912386,0.01179355,0.0007456631,0.04979085,0.06302627,0.489685],"genre_scores_gemma":[0.4219584,0.004136371,0.2031097,0.01261529,0.001838824,0.0009264674,0.02324498,0.02268075,0.3094892],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1125817,"threshold_uncertainty_score":0.3766233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03463846551360285,"score_gpt":0.3087285460835585,"score_spread":0.2740900805699557,"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."}}