{"id":"W341760123","doi":"10.1007/978-3-319-10437-9_13","title":"Linear Object Registration of Interventional Tools","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Image registration; Computer vision; Artificial intelligence; Point set registration; Ground truth; Point (geometry); Iterative closest point; Set (abstract data type); Object (grammar); Data set; Algorithm; Point cloud; Mathematics; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002677308,0.0001670301,0.0002126859,0.0002406883,0.00003132484,0.00005636138,0.0002774804,0.0001529496,0.00002632055],"category_scores_gemma":[0.00005559287,0.0001644358,0.00007715683,0.0001178985,0.0001594392,0.00007679146,0.00004285569,0.0002119135,0.000007773849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007752893,"about_ca_system_score_gemma":0.00005355218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000448618,"about_ca_topic_score_gemma":0.00003041975,"domain_scores_codex":[0.9989024,0.000007966359,0.0003486943,0.0002587773,0.0003400007,0.000142192],"domain_scores_gemma":[0.9993598,0.0001181424,0.00009575894,0.0002787062,0.0001130349,0.00003454692],"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":[0.000001567983,0.000003855049,0.0000100193,0.00007165531,0.000004877762,0.000002053339,0.00003446035,0.9226574,0.0002230044,0.005910281,0.00001956763,0.07106121],"study_design_scores_gemma":[0.00008847323,0.00007762471,0.00005187742,0.0003286915,0.000005363779,0.000005088992,3.350085e-8,0.9853159,0.002499475,0.0108408,0.0006120761,0.0001746599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001919564,0.00009392901,0.9956946,0.00003816902,0.0006855177,0.0001060062,0.000005550703,0.00004941028,0.003134884],"genre_scores_gemma":[0.9128332,0.00002438522,0.08622869,0.00008599213,0.0004606188,0.000001810396,0.00005561871,0.00003581197,0.0002738707],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9126412,"threshold_uncertainty_score":0.6705499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02026112543090988,"score_gpt":0.2351339113879011,"score_spread":0.2148727859569912,"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."}}