{"id":"W2150216687","doi":"10.1016/j.envres.2015.09.010","title":"Mercury speciation and selenium in toothed-whale muscles","year":2015,"lang":"en","type":"article","venue":"Environmental Research","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Japan Society for the Promotion of Science; Harvard Graduate School of Education","keywords":"Mercury (programming language); Selenium; Environmental chemistry; Whale; Genetic algorithm; Mercury poisoning; Environmental science; Chemistry; Fishery; Biology; Ecology; Toxicity; 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":[0.0001999016,0.0002700532,0.0002216603,0.0005732622,0.0005950718,0.000487802,0.0003045718,0.0004669057,0.001583164],"category_scores_gemma":[0.0003449481,0.0002647851,0.0002426697,0.0003248215,0.0004592683,0.0004636794,0.0004325573,0.000247705,0.0002822555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003070032,"about_ca_system_score_gemma":0.0002968416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01391216,"about_ca_topic_score_gemma":0.01968025,"domain_scores_codex":[0.9998984,0.00001569371,0.000009107107,0.00003826349,0.00001565781,0.00002291611],"domain_scores_gemma":[0.9998771,0.00002649031,0.00001975416,0.000008268786,0.00004799146,0.00002025983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004302325,0.0000914718,0.1807387,0.0002544934,0.0002778532,0.0008450704,0.002305795,0.0004027674,0.7948135,0.0004796964,0.0001720222,0.01531625],"study_design_scores_gemma":[0.00004178241,0.001203442,0.6944348,0.00002882089,0.0002579501,0.001256694,0.005045281,0.001755032,0.292319,0.0003498652,0.003282387,0.00002502899],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991665,0.0002201383,0.0001126245,0.0000178213,0.000004307517,0.000001776458,0.00002850634,0.00000249644,0.0004458219],"genre_scores_gemma":[0.9951022,0.0001797342,0.0002657799,0.00002556672,0.000004742953,0.000003884225,0.00007984028,0.000004555377,0.004333584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01391216,"threshold_uncertainty_score":0.02766234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08176497019343595,"score_gpt":0.3428836425354016,"score_spread":0.2611186723419656,"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."}}