{"id":"W4247304939","doi":"10.1515/iupac.85.0737","title":"Skimmer","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; Linguistics; Organic chemistry","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":[0.001334043,0.001825812,0.001612523,0.004655185,0.001118745,0.0045937,0.002596674,0.001657128,0.2267828],"category_scores_gemma":[0.01073988,0.0006187907,0.001640688,0.007480914,0.0003828623,0.003748835,0.003413063,0.001614439,0.3767551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00163358,"about_ca_system_score_gemma":0.003247848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01604918,"about_ca_topic_score_gemma":0.02969111,"domain_scores_codex":[0.9979499,0.0003097021,0.0003046184,0.0006291706,0.0005534199,0.000253212],"domain_scores_gemma":[0.9959521,0.000933843,0.0004352822,0.001038109,0.001393339,0.0002472994],"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":[0.00008308105,0.00001002206,0.000669988,0.0009003354,0.00002144187,0.00001626253,0.00002256994,0.0000848471,0.00006780644,0.0007318974,0.9904848,0.006906999],"study_design_scores_gemma":[0.00005353425,0.000007541821,0.001321955,0.0003669366,0.00001392732,0.00002886742,0.00005341191,0.00008554659,0.000149885,0.001022268,0.9968807,0.00001548998],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007943526,0.0001535842,0.0001219671,0.0001156137,0.0000482719,0.00001787282,0.9959559,0.0007999997,0.002707434],"genre_scores_gemma":[0.0003034541,0.0002067923,0.0003719349,0.0001279384,0.00001535349,0.00006872589,0.9957099,0.0002565593,0.00293943],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2267828,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.011909950911682,"score_gpt":0.392703670094491,"score_spread":0.380793719182809,"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."}}