{"id":"W3088667018","doi":"10.1002/fsn3.1882","title":"Consumers' attention on identification, nutritional compounds, and safety in heavy metals of Canadian sea cucumber in Chinese food market","year":2020,"lang":"en","type":"article","venue":"Food Science & Nutrition","topic":"Echinoderm biology and ecology","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Heavy metals; Identification (biology); Food safety; Chinese market; Business; Food market; Natural resource economics; Agricultural economics; Environmental science; Food science; Chemistry; Biology; China; Geography; Environmental chemistry; Agriculture; Economics; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003169719,0.0002527715,0.0001721173,0.0006434675,0.001137541,0.0005102475,0.0001840511,0.0003727057,0.004292425],"category_scores_gemma":[0.0005447784,0.00009217663,0.000217715,0.0007551001,0.0004734126,0.0003295659,0.0003507954,0.0002964304,0.0001623133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002177611,"about_ca_system_score_gemma":0.0009968418,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2523845,"about_ca_topic_score_gemma":0.3701173,"domain_scores_codex":[0.999769,0.00001783739,0.00001347406,0.00003816208,0.0001071425,0.00005433679],"domain_scores_gemma":[0.9995528,0.00004355118,0.0001132041,0.00001310983,0.0001786411,0.00009868227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006970038,0.0001577698,0.9192932,0.0006376742,0.00008555558,0.001238009,0.01017042,0.00012877,0.02625066,0.0004051108,0.004213766,0.03672205],"study_design_scores_gemma":[0.000006487986,0.0001024593,0.9869153,0.00003142408,0.00003764315,0.0001738476,0.006409632,0.0001805383,0.0009073287,0.0000428156,0.00516779,0.0000247368],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971135,0.0002502601,0.00004011113,0.0002319696,0.000007486556,0.000009730862,0.0002734461,0.000005195042,0.00206823],"genre_scores_gemma":[0.9970807,0.000328863,0.0001569786,0.0002017876,0.000008694929,0.000006887155,0.0002672222,0.000002412533,0.001946365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7476155,"threshold_uncertainty_score":0.5018309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02192073033422762,"score_gpt":0.2255587963222003,"score_spread":0.2036380659879727,"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."}}