{"id":"W1978010907","doi":"10.1118/1.1778834","title":"Metallic copper as a fiducial marker for both CT and PET","year":2004,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Cancer Foundation; University of Alberta","funders":"Alberta Cancer Foundation","keywords":"Fiducial marker; Positron emission tomography; Centroid; Medical imaging; Computer vision; Artificial intelligence; Tomography; Computer science; Modalities; Identification (biology); Nuclear medicine; Medical physics; Medicine; Radiology","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":[0.001818797,0.000842848,0.0008239881,0.001982048,0.000620344,0.001136648,0.001363292,0.001635485,0.002676222],"category_scores_gemma":[0.003474487,0.0007990558,0.0005135471,0.001797565,0.001128727,0.001067117,0.001117437,0.0008753726,0.001670272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006836505,"about_ca_system_score_gemma":0.0008129563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001473515,"about_ca_topic_score_gemma":0.00202671,"domain_scores_codex":[0.9984602,0.0007394952,0.00008847788,0.0002433231,0.0003622484,0.0001064245],"domain_scores_gemma":[0.998678,0.0004218375,0.0002096088,0.0003828071,0.0002265712,0.00008110634],"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":[0.002163074,0.0001243734,0.003939845,0.001729385,0.0001042012,0.003396388,0.0003873263,0.005827047,0.6526139,0.02081032,0.01225165,0.2966525],"study_design_scores_gemma":[0.0002099429,0.00223142,0.005693981,0.0003673229,0.0002827396,0.03507418,0.0002242301,0.02839763,0.7156765,0.005330043,0.2062781,0.0002338142],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07292058,0.02143713,0.8710847,0.001782106,0.0009684737,0.0007792161,0.0004706412,0.00356801,0.02698911],"genre_scores_gemma":[0.2755832,0.005120935,0.7072309,0.0003815448,0.0001543247,0.0005890736,0.0004487223,0.0005969813,0.00989431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002676222,"threshold_uncertainty_score":0.009618819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02293686740754113,"score_gpt":0.3334479572825366,"score_spread":0.3105110898749954,"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."}}