{"id":"W4311141708","doi":"10.1364/ao.477250","title":"2022 Optical Interference Coatings Conference: Manufacturing Problem Contest [Invited]","year":2022,"lang":"en","type":"article","venue":"Applied Optics","topic":"Surface Roughness and Optical Measurements","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"CONTEST; Transmittance; Interference (communication); Filter (signal processing); Optics; Materials science; Optical filter; Optical coating; Interference filter; Deposition (geology); Optoelectronics; Computer science; Telecommunications; Coating; Physics; Engineering; Nanotechnology; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002673178,0.0002694773,0.0002941018,0.00006776037,0.0002297874,0.00009749569,0.0003903787,0.00007344432,0.0005086499],"category_scores_gemma":[0.00001784395,0.000289308,0.0000535203,0.0001657685,0.00006785203,0.0000724055,0.0002843626,0.0006865868,0.00006698087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001663619,"about_ca_system_score_gemma":0.00002082013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004501787,"about_ca_topic_score_gemma":0.00000428153,"domain_scores_codex":[0.998306,0.00001846273,0.0003706707,0.0003129795,0.0004803355,0.0005115685],"domain_scores_gemma":[0.9993396,0.0001100289,0.00004731778,0.0002992628,0.00004259748,0.000161171],"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":[0.0002713981,0.0008119077,0.001231161,0.0008118068,0.0004813184,0.000150771,0.003626378,0.3436044,0.3343878,0.2082796,0.007361268,0.09898219],"study_design_scores_gemma":[0.007695793,0.001090057,0.002378742,0.0002769805,0.0004186651,0.0001412641,0.01241248,0.1931146,0.6827909,0.02046801,0.07355627,0.005656215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8313692,0.0001914501,0.03977232,0.0003816856,0.0009451665,0.001010803,0.00006137645,0.0009991121,0.1252688],"genre_scores_gemma":[0.9872013,0.00002430146,0.01206701,0.0002041402,0.00005181584,0.0002654486,0.00002936414,0.00006070544,0.00009591709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3484032,"threshold_uncertainty_score":0.9999559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02031864429665907,"score_gpt":0.2087395368612746,"score_spread":0.1884208925646155,"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."}}