{"id":"W3123589309","doi":"10.1109/tvcg.2021.3052167","title":"Shape-Driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Silhouette; Computer science; Artificial intelligence; Encoder; Artificial neural network; Pattern recognition (psychology); Context (archaeology)","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":[0.0006395674,0.0008319519,0.0009145332,0.0004343292,0.0002836323,0.0006013093,0.001164084,0.0009843264,0.002286688],"category_scores_gemma":[0.002243869,0.000449915,0.0005890777,0.0003400625,0.000735014,0.0009861257,0.0007916986,0.001175057,0.0003642828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001102043,"about_ca_system_score_gemma":0.0009111272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007479119,"about_ca_topic_score_gemma":0.007450513,"domain_scores_codex":[0.9997752,0.00004672812,0.00001043481,0.00008108665,0.00004773125,0.00003882644],"domain_scores_gemma":[0.9993483,0.0003007559,0.0001006583,0.00006211168,0.0001224465,0.00006565132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008080138,0.00007136996,0.0009722291,0.00004040853,0.00002160268,0.00006525747,0.00004980555,0.9422743,0.004676011,0.004362075,0.0008385668,0.04654754],"study_design_scores_gemma":[0.000003202094,0.00001018912,0.00003376843,0.000001482298,0.00000127348,0.000003243106,0.000001322127,0.9989821,0.0002260347,0.0006807192,0.00005517902,0.000001413964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06703352,0.0001952264,0.9296334,0.0001905282,0.00003965935,0.00006296196,0.00007182662,0.0007712713,0.002001571],"genre_scores_gemma":[0.832755,0.0001041111,0.1632017,0.0001374327,0.00002811449,0.0001448491,0.000160228,0.0001123644,0.003356333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007479119,"threshold_uncertainty_score":0.01487118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01895791875084982,"score_gpt":0.2686431331803413,"score_spread":0.2496852144294915,"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."}}