{"id":"W4399784576","doi":"10.25518/0037-9565.11904","title":"Necessity of a TDI Optical Corrector for ILMT Observations","year":2024,"lang":"en","type":"article","venue":"Bulletin de la Société Royale des Sciences de Liège","topic":"Calibration and Measurement Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Service Public de Wallonie; Université de Liège; Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS; Department of Science and Technology, Ministry of Science and Technology, India; York University","keywords":"Predictor–corrector method; Computer science; Mathematics; Applied mathematics","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":[0.001349729,0.0004006709,0.0006948056,0.001381911,0.001012433,0.0009864194,0.001092619,0.0004996281,0.00270945],"category_scores_gemma":[0.005889826,0.0003966716,0.000304798,0.001702879,0.0004618271,0.0007675472,0.0008600533,0.001092957,0.001524934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173532,"about_ca_system_score_gemma":0.001408059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007250542,"about_ca_topic_score_gemma":0.01250056,"domain_scores_codex":[0.9978492,0.0002965764,0.0001436237,0.0004612056,0.001070927,0.0001784969],"domain_scores_gemma":[0.9960796,0.0006100606,0.0005764407,0.001308492,0.001262591,0.0001627721],"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":[0.0008055768,0.0002057043,0.1842367,0.000420348,0.00009347586,0.0003770082,0.00119917,0.004944991,0.4764597,0.002029928,0.005436557,0.3237908],"study_design_scores_gemma":[0.0001283839,0.0006082241,0.425794,0.00009746093,0.0001734719,0.001845304,0.0005179377,0.04362442,0.4562548,0.0008181913,0.07000669,0.0001311685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5736496,0.0007993383,0.3956941,0.0007766997,0.0004361919,0.0005049409,0.002877471,0.01068245,0.0145791],"genre_scores_gemma":[0.7283784,0.0001930219,0.2645007,0.00018209,0.00008811579,0.0002288398,0.002120757,0.0009024734,0.003405521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007250542,"threshold_uncertainty_score":0.01441669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06531548220070321,"score_gpt":0.3336802673901981,"score_spread":0.2683647851894949,"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."}}