{"id":"W7055114446","doi":"","title":"Calibration de systèmes de caméras et projecteurs dans des applications de création multimédia","year":2010,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Magneto-Optical Properties and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Calibration; Linea; Reprojection error","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001784533,0.001511923,0.0007792339,0.001195472,0.00105754,0.002775154,0.001116169,0.001682224,0.01549636],"category_scores_gemma":[0.003904178,0.0009804864,0.0009831768,0.001249288,0.001128257,0.002441124,0.002429545,0.002188285,0.004603299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001347532,"about_ca_system_score_gemma":0.001889694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003852021,"about_ca_topic_score_gemma":0.006119646,"domain_scores_codex":[0.9982356,0.000200244,0.00005530757,0.0005040208,0.0008796925,0.0001251729],"domain_scores_gemma":[0.9980191,0.0003981082,0.0001470109,0.0006068524,0.0007194925,0.0001093089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003134377,0.00007620962,0.003851456,0.000412213,0.00008076753,0.0003693925,0.00142334,0.02849212,0.2438753,0.02442352,0.009685017,0.6869973],"study_design_scores_gemma":[0.00007069232,0.0006319484,0.01542677,0.0003212065,0.00009767633,0.002308416,0.001246361,0.2276535,0.3922015,0.009309055,0.3504623,0.0002706628],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02918338,0.0007960717,0.9434354,0.000486544,0.0004115116,0.0001471784,0.0001505238,0.003175341,0.02221409],"genre_scores_gemma":[0.1905021,0.001117013,0.7587478,0.0003124578,0.00009832501,0.0001736019,0.0005402922,0.0009884371,0.04752002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01549636,"threshold_uncertainty_score":0.05184054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003024630267889754,"score_gpt":0.1460867158060232,"score_spread":0.1430620855381334,"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."}}