{"id":"W2911035331","doi":"10.1016/j.nimb.2018.12.040","title":"Empirical simulations for further characterization of the Mars Science Laboratory Alpha Particle X-ray Spectrometer: An introduction to the ACES program","year":2019,"lang":"en","type":"article","venue":"Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Canadian Space Agency","keywords":"Mars Exploration Program; Spectrometer; Characterization (materials science); Exploration of Mars; Calibration; Software; Physics; Remote sensing; Computer science; Computational physics; Aerospace engineering; Environmental science; Astrobiology; Engineering; Optics; Geology","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":[0.0009090329,0.00007863285,0.0001086308,0.00008311831,0.0003882139,0.0002483835,0.0001203487,0.00001688291,0.00006494539],"category_scores_gemma":[0.00001648776,0.00004763944,0.00001206517,0.0006513306,0.0001885022,0.0009432727,0.00006180885,0.000116736,0.000003459688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000027876,"about_ca_system_score_gemma":0.00004563126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005676209,"about_ca_topic_score_gemma":0.00001064416,"domain_scores_codex":[0.9989584,0.0002137512,0.0001695146,0.0002620503,0.0002059852,0.0001902795],"domain_scores_gemma":[0.9994397,0.00005350918,0.00008257896,0.0002001158,0.0001735234,0.00005056917],"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":[0.00009197114,0.0001372118,0.009537346,0.000009858732,0.00001295278,1.164983e-8,0.001177217,0.0002149338,0.9602293,0.0009801183,0.00001312922,0.0275959],"study_design_scores_gemma":[0.001265786,0.002324261,0.2176047,0.0001054962,0.00004264959,0.000003539397,0.006756448,0.0401746,0.6687813,0.003138566,0.05938675,0.0004159246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968514,5.619591e-7,0.0009291662,0.0008783556,0.0004292069,0.0008219486,0.0000645668,0.000009408738,0.00001537168],"genre_scores_gemma":[0.9953258,0.000002661614,0.004155406,0.00003426718,0.0003409632,0.0000615093,0.00003888694,0.000008978517,0.00003154844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2914481,"threshold_uncertainty_score":0.2985866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04352284435715667,"score_gpt":0.408044115195502,"score_spread":0.3645212708383454,"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."}}