{"id":"W2285472722","doi":"10.1111/risa.12578","title":"Improving Risk Assessment Calculations for Traditional Foods Through Collaborative Research with First Nations Communities","year":2016,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Heavy Metals in Plants","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Assembly of First Nations; Northern Lakes College; Intrinsik (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Risk assessment; Environmental health; Risk analysis (engineering); Business; Computer science; Medicine; Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.02786541,0.001524243,0.001010923,0.004935046,0.00245266,0.003067248,0.003146144,0.001153369,0.005596842],"category_scores_gemma":[0.06554686,0.0006588588,0.002091989,0.0034045,0.0006924773,0.002762361,0.004143866,0.00131192,0.0008098829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004417546,"about_ca_system_score_gemma":0.01156254,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1417232,"about_ca_topic_score_gemma":0.1488971,"domain_scores_codex":[0.9818401,0.01111422,0.0007504431,0.001633314,0.004071875,0.0005900406],"domain_scores_gemma":[0.9655018,0.01522021,0.002515973,0.00352353,0.01249923,0.0007391171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.000666172,0.0009114378,0.1380729,0.001108721,0.00130174,0.001023615,0.004148248,0.3097422,0.0067577,0.0325742,0.01352629,0.4901668],"study_design_scores_gemma":[0.0002833038,0.000814933,0.04544653,0.001038599,0.0007988797,0.0006860952,0.006342569,0.8174641,0.0113884,0.04687363,0.06847689,0.0003860843],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1722993,0.001392549,0.7744495,0.001151021,0.0001645518,0.002390346,0.002423508,0.001423614,0.04430563],"genre_scores_gemma":[0.34496,0.0008394999,0.645626,0.0002170612,0.00003723768,0.001555868,0.002358943,0.0002209713,0.004184374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8582768,"threshold_uncertainty_score":0.2817965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1222435502735657,"score_gpt":0.3796634395345436,"score_spread":0.2574198892609778,"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."}}