{"id":"W2293319287","doi":"10.1038/srep19542","title":"Tuning and Freezing Disorder in Photonic Crystals using Percolation Lithography","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Photonic Crystals and Applications","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Multidisciplinary University Research Initiative; U.S. Air Force; Air Force Office of Scientific Research; Federal Railroad Administration; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense","keywords":"Percolation (cognitive psychology); Capillary action; Materials science; Wetting; Photonic crystal; Photonics; Lithography; Porous medium; Microfluidics; Porosity; Nanotechnology; Evaporation; Chemical physics; Optoelectronics; Composite material; Chemistry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005142281,0.00009299834,0.000114237,0.0001424593,0.0002519173,0.0001527533,0.00005023283,0.00002192352,0.0001736153],"category_scores_gemma":[0.00000810783,0.00007087812,0.00005014184,0.0003829078,0.0001226763,0.0002511592,0.00005378206,0.00004881314,0.000001516646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000205159,"about_ca_system_score_gemma":0.00006092249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002301321,"about_ca_topic_score_gemma":0.00003125231,"domain_scores_codex":[0.9988889,0.00001882454,0.000294029,0.0004399472,0.0001398951,0.0002184076],"domain_scores_gemma":[0.9993724,0.00002917673,0.0001489848,0.000348674,0.00003877834,0.00006193911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001072843,0.00004458016,0.2169235,0.00000480209,0.000006803598,0.000004174894,0.0003093284,0.00003533694,0.7724876,0.0008530405,0.0000450837,0.009284701],"study_design_scores_gemma":[0.002825623,0.00006915641,0.09435133,0.001490422,0.0001250478,0.0001735293,0.005513399,0.0307986,0.1672211,0.4596944,0.2351518,0.002585664],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888944,0.0001075384,0.009232368,0.00004984586,0.0002700396,0.0002286712,0.000005568453,0.00001897572,0.001192569],"genre_scores_gemma":[0.9990487,0.000002047784,0.0006333295,0.000003020856,0.00002883882,0.00003078909,0.000009595722,0.000009673624,0.0002339726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6052665,"threshold_uncertainty_score":0.2890326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273654435330144,"score_gpt":0.2564333390419793,"score_spread":0.2436967946886779,"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."}}