{"id":"W2973393326","doi":"10.1364/oe.27.028279","title":"Extrapolating from lens design databases using deep learning","year":2019,"lang":"en","type":"article","venue":"Optics Express","topic":"Advanced optical system design","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Lens (geology); Generalization; Deep learning; Artificial neural network; Artificial intelligence; Point (geometry); Machine learning; Optics","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":[0.0007460123,0.001540098,0.0008544636,0.001684615,0.000232292,0.001157678,0.00159187,0.001135736,0.003037042],"category_scores_gemma":[0.003536738,0.0008098578,0.0009404038,0.001125221,0.0004526743,0.001834862,0.0009458975,0.00118824,0.00116489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009804827,"about_ca_system_score_gemma":0.0007915997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003505732,"about_ca_topic_score_gemma":0.008009296,"domain_scores_codex":[0.9994676,0.00007901478,0.00003295604,0.0001464291,0.0002230554,0.00005093585],"domain_scores_gemma":[0.9985998,0.0005398787,0.0001598124,0.0003108738,0.0003402835,0.00004928086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002238796,0.0001989895,0.005225383,0.0002519483,0.00009664839,0.0002603755,0.00007639999,0.7202232,0.008154021,0.004011923,0.006082049,0.2551952],"study_design_scores_gemma":[0.00001030127,0.00004486124,0.0003458345,0.00001657333,0.00001069601,0.00002952767,0.00001639353,0.988969,0.004141882,0.004812188,0.001593049,0.000009730064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1032785,0.001471881,0.8839385,0.0004238999,0.00007601082,0.00009393963,0.001859189,0.003968744,0.004889376],"genre_scores_gemma":[0.7388687,0.000840763,0.2489459,0.0003740319,0.00006575645,0.0001772036,0.005652873,0.000230595,0.004844098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003505732,"threshold_uncertainty_score":0.01015991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04924393065653455,"score_gpt":0.2558307878711704,"score_spread":0.2065868572146358,"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."}}