{"id":"W4230746039","doi":"10.1515/iupac.85.0304","title":"Accelerating Potential","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; National Research Council Canada","funders":"","keywords":"Chemical nomenclature; Terminology; Mass spectrometry; Chemistry; Standardization; Accelerator mass spectrometry; Analytical Chemistry (journal); Computer science; Environmental chemistry; Chromatography; Organic chemistry; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001146712,0.001412018,0.001074411,0.00401371,0.001087222,0.00437427,0.002094402,0.001176987,0.1436465],"category_scores_gemma":[0.01316001,0.0004595299,0.001921282,0.00734437,0.0002948531,0.004761518,0.002506779,0.001833425,0.1512167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001751529,"about_ca_system_score_gemma":0.003117725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02073819,"about_ca_topic_score_gemma":0.03489013,"domain_scores_codex":[0.9984134,0.0002561399,0.0001774234,0.0004941069,0.0004395608,0.0002193466],"domain_scores_gemma":[0.9960983,0.001126975,0.0003619443,0.001016824,0.001131397,0.0002645951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008230477,0.00001614019,0.002290406,0.0007541889,0.00003957388,0.00002366231,0.00003085323,0.0003233907,0.00005106016,0.003369533,0.9749415,0.01807752],"study_design_scores_gemma":[0.00005058497,0.000008032663,0.002531052,0.0003567639,0.00002415508,0.00005227569,0.00009416851,0.0002879765,0.0001368915,0.003709435,0.9927312,0.0000175636],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005000223,0.00077408,0.0004883901,0.0005371267,0.0001550224,0.00003227099,0.9845315,0.001210155,0.01177146],"genre_scores_gemma":[0.002506894,0.0009353732,0.001410862,0.0003871178,0.00005511904,0.0001205255,0.9871511,0.00036229,0.007070749],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1436465,"threshold_uncertainty_score":0.4805454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01502853031942719,"score_gpt":0.3847283753747967,"score_spread":0.3696998450553695,"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."}}