{"id":"W2920279712","doi":"10.1049/el.2019.0064","title":"Dynamic length colour palettes","year":2019,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Palette (painting); Cluster analysis; Computer science; Artificial intelligence; Image (mathematics); Range (aeronautics); Computer vision; Mathematics; Engineering","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":[0.001194351,0.0009864822,0.0008824017,0.001996857,0.0008148238,0.00200688,0.001702009,0.001063437,0.01375556],"category_scores_gemma":[0.00790914,0.0006458751,0.001292431,0.001686285,0.001111692,0.002517123,0.00228697,0.00164161,0.00439777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000636291,"about_ca_system_score_gemma":0.0005954538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000845701,"about_ca_topic_score_gemma":0.001720088,"domain_scores_codex":[0.9989737,0.0002210418,0.00006071227,0.0002536979,0.0003940249,0.00009674482],"domain_scores_gemma":[0.9970084,0.001065288,0.0001487445,0.0008632222,0.0007565756,0.0001576952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00159497,0.0003047017,0.004886293,0.001454141,0.0001388823,0.0005148311,0.0009861701,0.08452452,0.08585861,0.08470768,0.05590913,0.6791201],"study_design_scores_gemma":[0.0002265383,0.0004354771,0.004262628,0.000403214,0.00009240965,0.0011308,0.0005287935,0.609718,0.09518251,0.1200182,0.1676997,0.0003018258],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03565702,0.001155846,0.9367816,0.0003134847,0.0004922424,0.0005563662,0.002582279,0.008334959,0.01412606],"genre_scores_gemma":[0.2578776,0.0007138284,0.7218741,0.0003357559,0.000121233,0.001135351,0.004112632,0.002456637,0.01137285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01375556,"threshold_uncertainty_score":0.04601693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002127040018337832,"score_gpt":0.2108486088370682,"score_spread":0.2087215688187304,"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."}}