{"id":"W4205379544","doi":"10.1364/fio.2021.jth5a.1","title":"Optimizing direct-field acceleration of electrons by tuning the Gouy phase","year":2021,"lang":"en","type":"article","venue":"Frontiers in Optics + Laser Science 2021","topic":"Laser-Plasma Interactions and Diagnostics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Acceleration; Paraxial approximation; Physics; Electron; Phase (matter); Particle acceleration; Laser; Field (mathematics); Computational physics; Optics; Atomic physics; Beam (structure); Nuclear physics; Classical mechanics; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002949515,0.0001205738,0.0001783748,0.00009548087,0.0002740379,0.0002113018,0.000319912,0.00003197626,0.0004249237],"category_scores_gemma":[0.0001653901,0.0001029405,0.00006511819,0.0008865384,0.0001547974,0.0004299637,0.0001048408,0.00023799,0.000008207288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006345131,"about_ca_system_score_gemma":0.0003376787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009958496,"about_ca_topic_score_gemma":0.00001977815,"domain_scores_codex":[0.9988081,0.00004213144,0.0002701962,0.0002903931,0.0002565994,0.0003325587],"domain_scores_gemma":[0.9990906,0.0002087726,0.0001122979,0.0003319301,0.000182798,0.0000736459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002350357,0.008789314,0.08723117,0.0000864116,0.0005068119,0.00009411142,0.01013431,0.1120976,0.278497,0.0426056,0.2948401,0.1648825],"study_design_scores_gemma":[0.0008239319,0.0001463977,0.00005073966,0.00007304916,0.0000468168,0.000001671291,0.005738886,0.1384599,0.8428563,0.000844197,0.01069441,0.000263607],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5419792,0.0002130183,0.3883836,0.001657358,0.002869894,0.0003061714,0.0001307926,0.00001979989,0.06444015],"genre_scores_gemma":[0.9777033,0.00005060402,0.0209224,0.00008402496,0.00009007564,0.00002033103,0.0000405553,0.000009708578,0.001079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5643593,"threshold_uncertainty_score":0.4652616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008714195013433824,"score_gpt":0.2662743535107192,"score_spread":0.2575601584972854,"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."}}