{"id":"W2591998777","doi":"10.1038/srep40415","title":"Laser-Accelerated Proton Beams as Diagnostics for Cultural Heritage","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Laser-Plasma Interactions and Diagnostics","field":"Physics and Astronomy","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Lawrence Livermore National Laboratory; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Università della Calabria; Ministero dell’Istruzione, dell’Università e della Ricerca; Compute Canada; European Social Fund; Regione Calabria; U.S. Department of Energy","keywords":"Proton; Laser; Sample (material); Cultural heritage; Materials science; Irradiation; Artifact (error); Optics; Nuclear engineering; Computer science; Physics; Nuclear physics; Artificial intelligence","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":[0.0004281792,0.0004109151,0.0002629847,0.0006322092,0.0002469777,0.0006863637,0.0005963999,0.0009433972,0.001279321],"category_scores_gemma":[0.0005173824,0.0002802609,0.0002069674,0.0004818047,0.0007733977,0.0007190045,0.0006524067,0.0006291158,0.0003403163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003885209,"about_ca_system_score_gemma":0.0001938054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002375929,"about_ca_topic_score_gemma":0.0004190988,"domain_scores_codex":[0.9997248,0.00008565748,0.000006407015,0.00004687934,0.0001104652,0.00002569546],"domain_scores_gemma":[0.9997203,0.0001277971,0.00004784415,0.00003801656,0.00004850179,0.00001765735],"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":[0.0003913365,0.00003839498,0.003253187,0.0006798324,0.00006673451,0.0006305075,0.0003452417,0.003495498,0.9388495,0.009991536,0.0007469803,0.04151132],"study_design_scores_gemma":[0.00003412989,0.0003363748,0.004383424,0.00007743263,0.0000482509,0.001219918,0.000219029,0.01155515,0.9621406,0.002450052,0.01748187,0.00005373878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7669466,0.03699248,0.1728591,0.0008174737,0.0004157663,0.0002422871,0.0006098897,0.0008437135,0.02027263],"genre_scores_gemma":[0.924386,0.005536927,0.06637739,0.000177086,0.00007534277,0.00009178625,0.0001379363,0.00005463268,0.003162846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001279321,"threshold_uncertainty_score":0.004279792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907114387256043,"score_gpt":0.314605591588604,"score_spread":0.2855344477160436,"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."}}