{"id":"W4236892971","doi":"10.1515/iupac.78.0154","title":"Attractant","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Pesticide; Computer science; Management science; Engineering; Ecology; Chemistry; Biology; Data mining; Linguistics","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":[0.0009806075,0.001880998,0.001344903,0.005601143,0.00102466,0.002787763,0.002423484,0.001765104,0.1651576],"category_scores_gemma":[0.008781366,0.0005875684,0.001238949,0.009937699,0.0003824045,0.002560498,0.002464369,0.00168765,0.1771133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001808914,"about_ca_system_score_gemma":0.003104903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02652087,"about_ca_topic_score_gemma":0.05874184,"domain_scores_codex":[0.9985201,0.0002328602,0.0002863265,0.0004505043,0.0003396245,0.0001706658],"domain_scores_gemma":[0.9966437,0.001027061,0.0004932621,0.0006000091,0.0009655927,0.0002704044],"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":[0.00004403811,0.000009554476,0.0007263307,0.0008883494,0.00001447647,0.00001568274,0.00002664541,0.00008820454,0.00007424076,0.0005872532,0.99431,0.003215078],"study_design_scores_gemma":[0.00006206962,0.00001002616,0.002281239,0.0004582903,0.00001587959,0.00004354484,0.00007246756,0.0001031154,0.00009962172,0.0007644741,0.9960711,0.0000182675],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006471229,0.00008173344,0.00004625026,0.00006632162,0.00001609072,0.00001023775,0.9987277,0.0001085887,0.0008783218],"genre_scores_gemma":[0.0002255103,0.00009761396,0.0002061026,0.00008672201,0.000007577135,0.00008046884,0.998174,0.00005322679,0.001068853],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1651576,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104393649935271,"score_gpt":0.4308220117704345,"score_spread":0.4097780752710818,"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."}}