{"id":"W7154076678","doi":"10.1145/3772318.3808949","title":"10.1145/3772318.3808949","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Session (web analytics); Data visualization; Visualization; Component (thermodynamics); Data collection","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002098229,0.003771396,0.002591738,0.003138369,0.002577156,0.005697608,0.003141277,0.004972995,0.9434683],"category_scores_gemma":[0.00340296,0.002067386,0.00145006,0.01028693,0.001683702,0.01061349,0.006505059,0.002938571,0.965573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002509029,"about_ca_system_score_gemma":0.0013308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02015326,"about_ca_topic_score_gemma":0.01475226,"domain_scores_codex":[0.9991666,0.00005650649,0.00007090937,0.0002459141,0.0002867325,0.000173445],"domain_scores_gemma":[0.9981256,0.0003808377,0.00007656566,0.0006339357,0.0004334506,0.0003495841],"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":[0.000240213,0.0001766395,0.0004588564,0.0005508609,0.0000485342,0.0002014663,0.00009982381,0.0007280804,0.001378571,0.006031974,0.702047,0.2880379],"study_design_scores_gemma":[0.00004160311,0.00003512553,0.0008209555,0.0002793584,0.00005334008,0.0001550429,0.0000890638,0.001054666,0.0006135399,0.001707683,0.9951082,0.00004131968],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002406813,0.005948671,0.01780733,0.001533486,0.002762832,0.0005581771,0.0193097,0.02509789,0.9245751],"genre_scores_gemma":[0.004062393,0.002787115,0.002804551,0.0007644393,0.0001734852,0.0002496591,0.0113181,0.002843296,0.9749969],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.05653167,"threshold_uncertainty_score":0.08063549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001048734582395,"score_gpt":0.2232405212365728,"score_spread":0.2132300338907489,"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."}}