{"id":"W4396701086","doi":"10.11159/iceptp24.149","title":"Mercury Transfer in the Marine Food Chain Rationalizing Safe Consumption of Seafood","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Food chain; Mercury (programming language); Environmental science; Food safety; Business; Computer science; Chemistry; Food science; Ecology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002182337,0.0003122301,0.0002147491,0.0005407822,0.0004151127,0.0006179049,0.0003441192,0.0003331924,0.0009310791],"category_scores_gemma":[0.0002410944,0.0001023949,0.0001670099,0.00050504,0.0002693014,0.0003351216,0.0004919232,0.0002278505,0.0002807431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005728755,"about_ca_system_score_gemma":0.0006439212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005282757,"about_ca_topic_score_gemma":0.007384861,"domain_scores_codex":[0.9997986,0.0000342333,0.0000119879,0.00004360928,0.0000771655,0.00003438827],"domain_scores_gemma":[0.9998738,0.000008655301,0.00004916481,0.000007142405,0.00005010764,0.00001126102],"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":[0.0003402579,0.0001806103,0.08173745,0.0004795395,0.00004756178,0.0004887635,0.0005731134,0.002583587,0.8446975,0.0008942204,0.0005175136,0.06745988],"study_design_scores_gemma":[0.00002742817,0.003504465,0.4605317,0.0001893202,0.0001558616,0.0006490415,0.002767117,0.008519436,0.498122,0.003609296,0.02186646,0.00005782985],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870273,0.002043361,0.006636633,0.0003461457,0.00001909069,0.00006892821,0.0002326631,0.00009046835,0.003535375],"genre_scores_gemma":[0.9879746,0.001453372,0.007985981,0.0001135801,0.00001137897,0.0000525689,0.0001799529,0.00001168153,0.002216743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005282757,"threshold_uncertainty_score":0.01050401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013405325804701,"score_gpt":0.2117679464480286,"score_spread":0.2016338931899816,"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."}}