{"id":"W2885229136","doi":"","title":"Canadian High-energy Neutron Spectrometry System (chenss)","year":2006,"lang":"en","type":"article","venue":"CERN Document Server (European Organization for Nuclear Research)","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Mass spectrometry; Computer science; Chemistry; Chromatography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003246008,0.001615139,0.001416604,0.007679904,0.008467826,0.002447982,0.003386422,0.001819139,0.08133612],"category_scores_gemma":[0.003648537,0.0008779872,0.0006709727,0.006400175,0.001163335,0.001914384,0.002599903,0.001831406,0.02113892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03021509,"about_ca_system_score_gemma":0.06448498,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8435314,"about_ca_topic_score_gemma":0.9302194,"domain_scores_codex":[0.9962487,0.0002205993,0.00006484023,0.0006952137,0.002276259,0.0004943157],"domain_scores_gemma":[0.9920884,0.0002667127,0.0002906609,0.0006274414,0.005967242,0.0007594445],"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":[0.001543796,0.0001761705,0.01671905,0.0006255597,0.0001669628,0.0002522756,0.0002938465,0.003251194,0.03280845,0.01832382,0.7712597,0.1545792],"study_design_scores_gemma":[0.0004861086,0.0001139628,0.02767541,0.0001170252,0.0001825223,0.0004919334,0.0002218415,0.01368321,0.03929998,0.003089119,0.9143673,0.0002715865],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06835154,0.007760087,0.06682055,0.009389635,0.002269653,0.001581572,0.265897,0.04266585,0.5352641],"genre_scores_gemma":[0.332402,0.00492164,0.1453817,0.003178782,0.0005875466,0.000718566,0.1396883,0.004231946,0.3688896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1564686,"threshold_uncertainty_score":0.3147802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008821219057937554,"score_gpt":0.2189741755913946,"score_spread":0.2101529565334571,"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."}}