{"id":"W6894317379","doi":"10.5683/sp3/en44uj","title":"Visualizing DLI data: The Quick and Dirty Way","year":2016,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Visualization; Data visualization; Information visualization; Data exploration; Visual analytics","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":[0.004726855,0.002706471,0.001471321,0.006621867,0.001774573,0.005113353,0.003389365,0.001611734,0.01919953],"category_scores_gemma":[0.02207031,0.001153555,0.001570892,0.008450444,0.0009494745,0.006107083,0.007163826,0.003583604,0.03184203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002395245,"about_ca_system_score_gemma":0.002493704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0257129,"about_ca_topic_score_gemma":0.07599693,"domain_scores_codex":[0.9948375,0.001074104,0.000775995,0.0008592189,0.002018536,0.0004346846],"domain_scores_gemma":[0.9853303,0.00236736,0.0007150125,0.00648726,0.004001912,0.001098201],"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":[0.00008487066,0.00003892872,0.001200826,0.0004390926,0.00002818572,0.00003319351,0.0003034117,0.0002280746,0.0004780446,0.001044271,0.9834942,0.01262681],"study_design_scores_gemma":[0.00008559515,0.00002371781,0.003646733,0.0002784846,0.00002158483,0.0001234945,0.0006942282,0.001569751,0.002370543,0.003886198,0.9872316,0.00006815707],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002323349,0.0005598431,0.008062535,0.001671218,0.000541461,0.0001894558,0.9475678,0.03106251,0.008021875],"genre_scores_gemma":[0.004494976,0.0002520308,0.01518071,0.0002272086,0.00004409494,0.0003439665,0.9749755,0.002357016,0.002124532],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0257129,"threshold_uncertainty_score":0.06422877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05575880909106048,"score_gpt":0.3378958780736147,"score_spread":0.2821370689825542,"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."}}