{"id":"W100026819","doi":"10.1007/978-3-642-33179-4_65","title":"Sketch-Line Interactions for 3D Image Visualization and Analysis","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sketch; Computer science; Computer vision; Line (geometry); Visualization; Line segment; Object (grammar); Computer graphics (images); Artificial intelligence; Volume (thermodynamics); Image (mathematics); Orientation (vector space); Surface (topology); Algorithm; Geometry; Mathematics","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":[0.0003638174,0.001853648,0.0006205536,0.0008363019,0.0004160627,0.001953889,0.001679176,0.001278332,0.07432442],"category_scores_gemma":[0.002089925,0.0006632857,0.0006693358,0.0009905437,0.0004194363,0.002032651,0.002549736,0.001066701,0.01152456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002092118,"about_ca_system_score_gemma":0.0001780041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004155233,"about_ca_topic_score_gemma":0.0007818245,"domain_scores_codex":[0.9995946,0.00007816005,0.00002085334,0.00005521906,0.0002167965,0.00003441944],"domain_scores_gemma":[0.9992188,0.0004134026,0.00002923181,0.0001609259,0.0001264883,0.00005114413],"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":[0.0004783702,0.000134937,0.0003628577,0.001146317,0.0000731652,0.000449034,0.0007397219,0.01288111,0.1950929,0.03281189,0.04716262,0.708667],"study_design_scores_gemma":[0.0002029304,0.0003725836,0.001446641,0.000368139,0.000130495,0.002633324,0.000498839,0.3158772,0.2259046,0.04327628,0.4090999,0.0001890355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00395727,0.0009991366,0.9782847,0.00009058928,0.0001195667,0.00007215812,0.0002385162,0.006604337,0.009633804],"genre_scores_gemma":[0.1377797,0.003029722,0.8024777,0.0002000396,0.0001661967,0.0003515424,0.001242679,0.00321547,0.05153702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07432442,"threshold_uncertainty_score":0.24864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02258247950018198,"score_gpt":0.3129505038725972,"score_spread":0.2903680243724152,"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."}}