{"id":"W2110028027","doi":"10.1109/infovis.2004.47","title":"Metric-Based Network Exploration and Multiscale Scatterplot","year":2004,"lang":"en","type":"article","venue":"IEEE Symposium on Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Artificial intelligence; Metric (unit); Image (mathematics); Image segmentation; Segmentation; Process (computing); Pattern recognition (psychology); Image processing; Computer vision; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000354062,0.0001932953,0.0001560458,0.0004872823,0.0002907254,0.0006656211,0.0003002752,0.00009919718,0.000007465823],"category_scores_gemma":[0.00005979865,0.0001930686,0.00004145414,0.001368545,0.00003720551,0.005427359,0.00004816619,0.00007434841,0.0002110902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001075729,"about_ca_system_score_gemma":0.00008646226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001656883,"about_ca_topic_score_gemma":0.000005421089,"domain_scores_codex":[0.9984193,0.00006830095,0.0005353664,0.0002381071,0.0004965408,0.0002423959],"domain_scores_gemma":[0.9988898,0.00005135273,0.0003054396,0.0003732362,0.0002540343,0.0001261425],"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":[0.00001610824,0.0001153711,0.000291181,0.00005662956,0.00001337399,7.75488e-7,0.001230742,0.6340137,0.0002119301,0.3570992,0.002826064,0.004124994],"study_design_scores_gemma":[0.001739195,0.0002571396,0.0004451991,0.00009602364,0.00001491344,0.000003942404,0.000055856,0.9733524,0.009440141,0.002389224,0.01182034,0.0003856238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001207702,0.000008191715,0.9949719,0.001579885,0.0005693099,0.0002981116,0.00001059572,0.0003739882,0.0009802892],"genre_scores_gemma":[0.9787383,0.0001056047,0.01209959,0.008137405,0.0001739766,0.00005514853,0.0006111644,0.00002182569,0.00005696873],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9828724,"threshold_uncertainty_score":0.787311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01762289522549168,"score_gpt":0.2818441891413416,"score_spread":0.2642212939158499,"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."}}