{"id":"W2110991707","doi":"10.1109/tvcg.2010.234","title":"Visual Comparability of 3D Regular Sampling and Reconstruction","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Comparability; Artificial intelligence; Computer science; Lattice (music); Cartesian coordinate system; Metric (unit); Visualization; Tetrahedron; Computer vision; Pattern recognition (psychology); Mathematics; Geometry; Physics; Combinatorics","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":[0.0003207458,0.0001322423,0.0001798998,0.0002771351,0.0001752186,0.00009623945,0.0001464157,0.0001101367,0.00001552566],"category_scores_gemma":[0.000006175148,0.0001335066,0.00003969513,0.0004237579,0.0002719215,0.0003954494,0.000006024226,0.0002220474,7.297812e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007222681,"about_ca_system_score_gemma":0.00002666785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001796099,"about_ca_topic_score_gemma":0.00001614564,"domain_scores_codex":[0.998858,0.00009749836,0.0003460702,0.0003420078,0.000237329,0.0001190656],"domain_scores_gemma":[0.9992358,0.0001285104,0.0001216288,0.0002401253,0.0001614117,0.0001124905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000307362,0.0005317731,0.001235546,0.0001639842,0.00006249869,0.000001420443,0.001284899,0.00007124127,0.008387844,0.2515211,0.00003646885,0.7366725],"study_design_scores_gemma":[0.0005099686,0.000265619,0.002537382,0.000052436,0.00001841028,0.00005810556,0.00002527635,0.9200103,0.07385233,0.0023888,0.00006717766,0.0002142369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.11993,0.000009330313,0.8791962,0.00002710841,0.0004779556,0.0001619021,0.000002721957,0.000183869,0.00001087836],"genre_scores_gemma":[0.8894554,0.00009899425,0.1100912,0.0002967044,0.00002927313,0.00001157744,0.000002659385,0.000008721679,0.000005551561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.919939,"threshold_uncertainty_score":0.5444241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0211933036311455,"score_gpt":0.3075293401034804,"score_spread":0.2863360364723349,"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."}}