{"id":"W4394879939","doi":"10.1063/5.0184919","title":"Optimizing laser coupling, matter heating, and particle acceleration from solids using multiplexed ultraintense lasers","year":2024,"lang":"en","type":"article","venue":"Matter and Radiation at Extremes","topic":"Laser-Plasma Interactions and Diagnostics","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"H2020 European Research Council; Nuclear Physics; European Regional Development Fund; Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; European Commission; Compute Canada","keywords":"Laser; Electron; Physics; Acceleration; Coupling (piping); Particle acceleration; Magnetic field; Radiation; Resistive touchscreen; Particle (ecology); Front (military); Optics; Particle-in-cell; Atomic physics; Materials science; Nuclear physics; Computer science; Classical mechanics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005189005,0.0001449298,0.000128347,0.00004839143,0.0002182816,0.000389831,0.00002943673,0.00003531257,0.004760116],"category_scores_gemma":[0.000005466496,0.0001293969,0.00004333012,0.00005882662,0.00003257049,0.0003376106,0.000033024,0.0000885804,0.0002946404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003343929,"about_ca_system_score_gemma":0.00001237403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007276308,"about_ca_topic_score_gemma":0.00002513651,"domain_scores_codex":[0.9992571,0.0000179956,0.0002104982,0.0002632325,0.00007821509,0.0001729189],"domain_scores_gemma":[0.9995595,0.0001841029,0.0000481553,0.00009849958,0.00002897571,0.0000807199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005090699,0.0001180132,0.8822472,0.00007548258,0.0002178566,0.00001119354,0.004235553,0.01992111,0.06005552,0.0002518971,0.02864944,0.00416582],"study_design_scores_gemma":[0.0009487033,0.00002619142,0.04451718,0.0001734029,0.0001518071,0.000006979131,0.001125735,0.8972583,0.04597586,0.0003615435,0.00902543,0.0004288216],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938365,0.0001235545,0.004244389,0.0006464744,0.000349535,0.0001225058,0.00009522143,0.00003945007,0.0005423365],"genre_scores_gemma":[0.9968789,0.00001464091,0.001195955,0.000423026,0.0002753437,0.00001557315,0.0001217673,0.00002410845,0.001050662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8773372,"threshold_uncertainty_score":0.9961497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01857230789536368,"score_gpt":0.2633018146431902,"score_spread":0.2447295067478266,"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."}}