{"id":"W2165597360","doi":"10.1109/tim.2006.876410","title":"Calibration of an Integrated Robotic Multimodal Range Scanner","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Calibration; Range (aeronautics); Computer science; Sensor fusion; Artificial intelligence; Computer vision; Scanner; Real-time computing; Engineering","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":[0.0006777198,0.000386645,0.0003785547,0.0004443887,0.0003140967,0.0006138102,0.001123049,0.0006964182,0.002256153],"category_scores_gemma":[0.001047394,0.000334445,0.0002906894,0.0003999752,0.0003865426,0.0007912246,0.001139663,0.0005659253,0.0005747623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005385045,"about_ca_system_score_gemma":0.001201638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001288632,"about_ca_topic_score_gemma":0.001968082,"domain_scores_codex":[0.9993375,0.00006253073,0.00001994618,0.000175069,0.0003544966,0.00005049213],"domain_scores_gemma":[0.9996207,0.00005069832,0.00003990941,0.0001199914,0.0001421498,0.00002655342],"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":[0.00043673,0.0001698742,0.007800675,0.000181178,0.00009853466,0.0004038896,0.0004671664,0.08125698,0.5906859,0.006083932,0.002366996,0.3100482],"study_design_scores_gemma":[0.00006278762,0.0009763473,0.02025537,0.00007109059,0.0001010305,0.001497593,0.0003312505,0.5880211,0.3622927,0.002948846,0.02329135,0.0001505367],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2071689,0.0001373515,0.7815575,0.0001508665,0.00007328033,0.0001665523,0.0002506745,0.004171678,0.006323151],"genre_scores_gemma":[0.5885873,0.00006416115,0.4077647,0.00008110931,0.00001635645,0.000130414,0.0002573308,0.00009379082,0.003004954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002256153,"threshold_uncertainty_score":0.007547617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03164458844174207,"score_gpt":0.2240004095024892,"score_spread":0.1923558210607471,"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."}}