{"id":"W6948161955","doi":"10.5061/dryad.95x69p8jd","title":"Spatial fingerprinting: horizontal fusion of multi-dimensional bio-tracers as solution to global food provenance problems","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medicinal Plants and Bioactive Compounds","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Canada First Research Excellence Fund","keywords":"Provenance; Legislation; Sensor fusion; Generality; Sustainability; Data integration; Geographic information system; Metadata; Food products","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003356748,0.0002218702,0.0002252877,0.00008899814,0.0005494416,0.0001310505,0.0006152522,0.0002136341,0.0008824509],"category_scores_gemma":[0.0005965807,0.0002174208,0.00008611166,0.0002375304,0.0001233148,0.000008575108,0.00156762,0.0002310457,0.0003538614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000107106,"about_ca_system_score_gemma":0.00003462384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002225964,"about_ca_topic_score_gemma":0.00002122415,"domain_scores_codex":[0.9981586,0.0001851712,0.0003041047,0.0006190229,0.0004175052,0.0003156348],"domain_scores_gemma":[0.9985744,0.000005883043,0.0002218731,0.0004384842,0.0005839308,0.0001754712],"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":[0.0001892458,0.000211664,0.0000024689,0.0001497457,0.0000823759,0.00001046161,0.0000210857,0.00002389878,0.1465606,0.00001309427,0.8381708,0.01456448],"study_design_scores_gemma":[0.0004804109,0.001490859,0.0001705039,0.0001728654,0.00002806229,0.00009776235,0.00003895973,0.0000497281,0.008073556,0.000006174439,0.9891564,0.0002347202],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03942431,0.0005039437,0.002223108,0.0003259121,0.000480152,0.0009309484,0.9546919,0.00006959382,0.001350129],"genre_scores_gemma":[0.08413622,0.0001643857,0.0002426856,0.000109225,0.0003855126,1.506538e-7,0.9146057,0.0002477835,0.0001083564],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1509856,"threshold_uncertainty_score":0.9662219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02600654305407499,"score_gpt":0.2593724269706263,"score_spread":0.2333658839165513,"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."}}