{"id":"W3129787858","doi":"10.1002/9780470027318.a9265","title":"Mercury Speciation in Foods","year":2018,"lang":"en","type":"other","venue":"Encyclopedia of Analytical Chemistry","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"","keywords":"Mercury (programming language); Methylmercury; Contaminated food; Environmental chemistry; Mercury contamination; Contamination; Environmental science; Chemistry; Ecology; Biology; Bioaccumulation; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001125235,0.0001819372,0.0003079945,0.0000356228,0.00001304393,0.000004564031,0.0001727695,0.0002269302,0.09276244],"category_scores_gemma":[0.0001565475,0.0001690538,0.00007497943,0.0002263301,0.0002792647,0.00003079441,0.000112998,0.0001263477,0.0009944842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008335539,"about_ca_system_score_gemma":0.00001863695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001352513,"about_ca_topic_score_gemma":0.00008162239,"domain_scores_codex":[0.9989175,0.00001429685,0.0002836639,0.0002661583,0.0003141153,0.0002042603],"domain_scores_gemma":[0.9994698,0.00003693827,0.0001579188,0.0002381134,0.00000574146,0.00009154507],"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":[0.00000397301,0.00004990699,0.01885539,0.00005669979,0.00002186469,0.00000345977,0.00005793725,0.000001077617,0.000105294,0.00001119796,0.9804063,0.0004268922],"study_design_scores_gemma":[0.0001522651,0.00001374245,0.005424133,0.00007051876,0.00003371086,9.648573e-7,0.00003785584,0.00003186633,0.00032376,0.0001321649,0.9935821,0.0001969597],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.005146969,0.00008145051,0.00001665566,0.00006801608,0.00008044386,0.0000748834,0.00001594231,0.00002714205,0.9944885],"genre_scores_gemma":[0.004396966,0.0005470215,0.000266981,0.00002925609,0.0004197384,0.000005658467,0.00002771733,0.00007535974,0.9942313],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.09176795,"threshold_uncertainty_score":0.9997833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009998745100934101,"score_gpt":0.2588589517583459,"score_spread":0.2488602066574118,"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."}}