{"id":"W4231101792","doi":"10.1515/iupac.78.0417","title":"Minor Consumption Crop","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Pesticide Residue Analysis and Safety","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glossary; Pesticide; Relation (database); Computer science; Data science; Ecology; Biology; Data mining","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":[0.0004931483,0.00128489,0.001113758,0.004210571,0.0006655114,0.001967728,0.001736685,0.0009385711,0.1317598],"category_scores_gemma":[0.005006843,0.0004149914,0.0009270925,0.009566562,0.0003035873,0.001971166,0.001420965,0.001224241,0.1307663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001360196,"about_ca_system_score_gemma":0.002014234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02930537,"about_ca_topic_score_gemma":0.05126271,"domain_scores_codex":[0.9991902,0.00009537374,0.0001387328,0.000282226,0.0002012062,0.00009226554],"domain_scores_gemma":[0.9979472,0.0004714709,0.0003453372,0.0004033434,0.0006962632,0.0001363709],"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":[0.00004903096,0.000009092585,0.001174891,0.0008525897,0.00001892524,0.00002186988,0.00002362392,0.0001213772,0.00009700625,0.0007015651,0.9923666,0.004563373],"study_design_scores_gemma":[0.00003497942,0.000005524745,0.003750587,0.0003064038,0.000009732963,0.00003621563,0.00006408807,0.00007417052,0.0001026546,0.0006078231,0.9949964,0.00001147544],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008695107,0.00006254273,0.00003195888,0.0000303497,0.00001218361,0.000005324057,0.9985195,0.000072248,0.001179032],"genre_scores_gemma":[0.0003585741,0.0001209439,0.0001450301,0.00005134896,0.000005564606,0.0000384223,0.9977154,0.00003977272,0.00152508],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1317598,"threshold_uncertainty_score":0.4407805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02332591247137229,"score_gpt":0.3661969559815841,"score_spread":0.3428710435102118,"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."}}