{"id":"W4226106373","doi":"","title":"Inferring the solution space of microscope objective lenses using deep learning","year":2022,"lang":"en","type":"article","venue":"Corpus Université Laval (Université Laval)","topic":"Advanced optical system design","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Lens (geology); Computer science; Optics; Extrapolation; Aperture (computer memory); Set (abstract data type); Microscope; Artificial intelligence; Deep learning; Algorithm; Computer vision; Mathematics; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002098768,0.0002680432,0.0003367681,0.0002705543,0.001063748,0.00002197775,0.0005087192,0.0000862877,0.000049425],"category_scores_gemma":[0.00003267091,0.0002961847,0.0001703647,0.000892633,0.0001168101,0.0003491468,0.0004995996,0.0005924877,0.000006344219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0016123,"about_ca_system_score_gemma":0.00008128831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001678389,"about_ca_topic_score_gemma":0.0004866741,"domain_scores_codex":[0.9984351,0.0002128802,0.0001970232,0.0003170405,0.0003873326,0.0004506372],"domain_scores_gemma":[0.9990734,0.000220649,0.0001616766,0.000313396,0.000120758,0.0001101339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001153986,0.00002785001,0.003438781,0.00005552305,0.0001446126,0.0001005188,0.008056549,0.5385679,0.4431022,0.002766709,0.00003556983,0.003588377],"study_design_scores_gemma":[0.005282938,0.001264567,0.009752894,0.0003326048,0.001082717,0.0005594288,0.08204681,0.3399144,0.508109,0.0005494126,0.04854095,0.002564238],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9255214,0.002051651,0.06180469,0.0001157571,0.0005270395,0.0004719747,0.0000263439,0.0004584301,0.009022691],"genre_scores_gemma":[0.9971511,0.0002495183,0.001264038,0.000009995629,0.00003777624,0.000001637518,0.000009067765,0.00005668809,0.001220222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1986535,"threshold_uncertainty_score":0.999949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008753026985540157,"score_gpt":0.18322803901212,"score_spread":0.1744750120265799,"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."}}