{"id":"W4387576234","doi":"10.1002/adpr.202300241","title":"Photochemically Engineered Large‐Area Arsenic Sulfide Micro‐Gratings for Hybrid Diffractive–Refractive Infrared Platforms","year":2023,"lang":"en","type":"article","venue":"Advanced Photonics Research","topic":"Optical Coatings and Gratings","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"Defense Sciences Office, DARPA; Advanced Research Projects Agency; University of Central Florida; Bentham-Moxon Trust; Massachusetts Institute of Technology; McGill University; U.S. Department of Defense","keywords":"Materials science; Infrared; Fabrication; Optics; Optoelectronics; Polarization (electrochemistry); Laser; Grating; Etching (microfabrication); Nanotechnology; Chemistry; Layer (electronics)","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":[0.000073026,0.0002332717,0.0000901486,0.0001194618,0.00009863877,0.0002193626,0.0002161461,0.0001948287,0.0006061058],"category_scores_gemma":[0.00005959868,0.0001794989,0.0001244095,0.0001011354,0.0001854728,0.0001290151,0.0001645933,0.0002384567,0.0002145407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003575786,"about_ca_system_score_gemma":0.0001816469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000638606,"about_ca_topic_score_gemma":0.002546935,"domain_scores_codex":[0.9999539,0.000003698356,0.000003096996,0.000008979418,0.00001872029,0.00001165975],"domain_scores_gemma":[0.9999588,0.000005810751,0.00001667144,0.000005181988,0.000004974238,0.000008573044],"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":[0.000009411185,0.00000888706,0.000050821,0.00001178605,0.000001722078,0.00002203503,0.00000994904,0.0001999904,0.9986034,0.0002242738,0.00005788397,0.0007998869],"study_design_scores_gemma":[0.000008757298,0.00006717249,0.0006030645,0.000001303624,0.000003351908,0.0000484742,0.00001222621,0.003152134,0.9947108,0.00004147876,0.001346508,0.000004729157],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886621,0.0002962448,0.007909612,0.00008575663,0.00004145717,0.00002939684,0.0001081301,0.0002159852,0.002651308],"genre_scores_gemma":[0.9891182,0.0001821523,0.009123947,0.00002035383,0.000006285378,0.00001504707,0.00005521054,0.00001958966,0.00145934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000638606,"threshold_uncertainty_score":0.002594471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04533372793940949,"score_gpt":0.3607865919214976,"score_spread":0.3154528639820882,"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."}}