{"id":"W4246611877","doi":"10.1145/2070781.2024202","title":"Slices","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Mitacs","keywords":"Computer science; Planar; Intersection (aeronautics); Set (abstract data type); Object (grammar); Artificial intelligence; Computer vision; Computer graphics (images)","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":[0.0003613049,0.0009211822,0.00060651,0.001138679,0.0005914,0.00221512,0.0009725731,0.0007533417,0.02117254],"category_scores_gemma":[0.002675522,0.0004474553,0.0008221705,0.001013617,0.0005857942,0.002313863,0.002061549,0.0006946017,0.00407434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004931201,"about_ca_system_score_gemma":0.0005931038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00116722,"about_ca_topic_score_gemma":0.002935739,"domain_scores_codex":[0.9996123,0.00005124299,0.00003402368,0.0001094835,0.0001483498,0.00004453958],"domain_scores_gemma":[0.999361,0.000150057,0.00006354765,0.0002506474,0.0001331993,0.00004169127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007305518,0.00008176374,0.004991573,0.000856779,0.0001094261,0.0004635292,0.001112834,0.04955481,0.05489361,0.2259414,0.02973626,0.6315274],"study_design_scores_gemma":[0.00008563624,0.0003552405,0.003857757,0.0003258459,0.000114707,0.001928019,0.001211947,0.4190932,0.07347383,0.2619323,0.2374932,0.0001283511],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03094104,0.0005802381,0.9382932,0.0001879245,0.0001022622,0.0002237546,0.001691999,0.005783655,0.02219599],"genre_scores_gemma":[0.3042789,0.0006232208,0.677965,0.0001680431,0.00004237691,0.0002360427,0.004634092,0.001831633,0.01022059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02117254,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07015500290185923,"score_gpt":0.2230740618613463,"score_spread":0.1529190589594871,"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."}}