{"id":"W4248274920","doi":"10.1515/iupac.80.0218","title":"Analytical Considerations","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Heavy Metal Exposure and Toxicity","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Lymphocyte; Beryllium; Cadmium; Mercury (programming language); Immunology; Chemistry; Biology; Computer science","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":[0.008647403,0.001917759,0.001832688,0.006077338,0.001456006,0.005003737,0.003597245,0.002246844,0.1095653],"category_scores_gemma":[0.06528988,0.0007417314,0.002551229,0.008498721,0.0007569813,0.002560917,0.002901999,0.002432782,0.07500251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002085503,"about_ca_system_score_gemma":0.006900141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01007634,"about_ca_topic_score_gemma":0.01580463,"domain_scores_codex":[0.989898,0.002176911,0.002509441,0.002466903,0.002358661,0.0005900105],"domain_scores_gemma":[0.9768443,0.008275021,0.002007609,0.005263142,0.006896896,0.0007130558],"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":[0.0005440881,0.00007372408,0.00478612,0.005951936,0.0002699291,0.0001241501,0.0001188869,0.0006570236,0.0004567292,0.003464173,0.948259,0.03529423],"study_design_scores_gemma":[0.000192675,0.00002799678,0.003722634,0.001617123,0.00008559143,0.0001231263,0.00009139993,0.0002675195,0.0003676199,0.003780599,0.9896952,0.00002836768],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004558592,0.0006226137,0.001366805,0.0005019995,0.000183343,0.0003529196,0.9909196,0.0008101988,0.004786605],"genre_scores_gemma":[0.002393917,0.0005455778,0.005874347,0.0007103282,0.00008549016,0.002292106,0.9849548,0.000421341,0.002722098],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1095653,"threshold_uncertainty_score":0.3665324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02080110920110378,"score_gpt":0.3934662909890745,"score_spread":0.3726651817879707,"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."}}