{"id":"W4289517857","doi":"10.20944/preprints202208.0038.v1","title":"Novel Fabrication Tools for Dynamic Compression Targets with Engineered Voids Using Photolithography Methods","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Laser-Matter Interactions and Applications","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"SLAC National Accelerator Laboratory; Basic Energy Sciences; U.S. Department of Energy; Division of Electrical, Communications and Cyber Systems; Los Alamos National Laboratory; National Nuclear Security Administration; National Science Foundation; Ministerio de Economía y Competitividad; Laboratory Directed Research and Development; Fusion Energy Sciences; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Office of Science","keywords":"Fabrication; Materials science; Void (composites); Photolithography; Nanotechnology; Dynamic range compression; Composite material","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000269484,0.0003506189,0.0002013318,0.0003063529,0.0002289957,0.0004500521,0.0005421884,0.0004115532,0.0009842414],"category_scores_gemma":[0.0004161778,0.0003661643,0.0001870264,0.0001973537,0.0003903351,0.0005327706,0.0004583564,0.0008225976,0.0004230836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005078459,"about_ca_system_score_gemma":0.0004219558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000436533,"about_ca_topic_score_gemma":0.001365924,"domain_scores_codex":[0.9997073,0.00001085196,0.0000232513,0.00005082771,0.0001737057,0.0000340549],"domain_scores_gemma":[0.9995579,0.00009546346,0.0001711467,0.00009967895,0.00005066966,0.00002517122],"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":[0.0000128558,0.00001821668,0.0001029909,0.00009607388,0.000003909337,0.00008251677,0.00005996952,0.0008881057,0.9915519,0.001807255,0.0002871482,0.005089073],"study_design_scores_gemma":[0.000006095384,0.00004906738,0.0005913508,0.000005971848,0.00000385078,0.0001512912,0.0000180275,0.005230828,0.9879931,0.0002772613,0.005661384,0.0000116513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5790635,0.001652329,0.3977296,0.0004737667,0.0003886363,0.0004867017,0.001221758,0.004941402,0.01404233],"genre_scores_gemma":[0.7323054,0.0005765902,0.26175,0.0001021329,0.00003437398,0.0003622127,0.0004224002,0.0003643743,0.004082557],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0009842414,"threshold_uncertainty_score":0.0036847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1207085752055985,"score_gpt":0.4130439590964951,"score_spread":0.2923353838908966,"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."}}