{"id":"W1990499660","doi":"10.1504/ijamc.2009.026851","title":"A 3D scanning system for biomedical purposes","year":2009,"lang":"en","type":"article","venue":"International Journal of Advanced Media and Communication","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Smoothing; Computer vision; Artificial intelligence; Laser scanning; Calibration; Transformation (genetics); Field (mathematics); Algorithm; Computer graphics (images); Laser; Optics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009445066,0.0009323726,0.0008944085,0.001658694,0.0007044899,0.001096625,0.001076754,0.001770711,0.04441911],"category_scores_gemma":[0.001113725,0.0005736131,0.0006771959,0.001478632,0.000510884,0.0009053969,0.001788091,0.001047955,0.01468221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004047218,"about_ca_system_score_gemma":0.001146512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008119077,"about_ca_topic_score_gemma":0.001231524,"domain_scores_codex":[0.9989457,0.0001449487,0.00005873382,0.0001931963,0.0006148555,0.00004269306],"domain_scores_gemma":[0.9989913,0.0002093695,0.00007984348,0.0003350248,0.0002853887,0.00009922175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000364456,0.00008630307,0.001253849,0.0006523085,0.00005594988,0.0004724082,0.0002769061,0.002046375,0.5713737,0.008544273,0.05436894,0.3605045],"study_design_scores_gemma":[0.0001498405,0.001021323,0.01014896,0.0001495871,0.0001648743,0.006961375,0.0001048658,0.03463656,0.2526247,0.003477318,0.690251,0.0003096784],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01860202,0.001929894,0.9144778,0.001163716,0.001292549,0.001098016,0.005513886,0.02381618,0.03210599],"genre_scores_gemma":[0.1370535,0.001672807,0.7901755,0.002395383,0.0004617114,0.002236332,0.005742903,0.001078625,0.05918333],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04441911,"threshold_uncertainty_score":0.1485967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02615744946426311,"score_gpt":0.310305430424462,"score_spread":0.2841479809601988,"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."}}