{"id":"W4241038853","doi":"10.1515/iupac.78.0360","title":"Hydrophilic","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Pesticide and Herbicide Environmental Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Computer science; Pesticide; Management science; Data science; Chemistry; Engineering; Ecology; 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":[0.001286281,0.001711373,0.00122013,0.004859815,0.0009390836,0.003193246,0.002442037,0.001910136,0.1800584],"category_scores_gemma":[0.0103202,0.0005487713,0.001413864,0.008280764,0.0004221622,0.002915431,0.002157623,0.001724934,0.1996322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001991499,"about_ca_system_score_gemma":0.003293866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01952418,"about_ca_topic_score_gemma":0.03985612,"domain_scores_codex":[0.9980043,0.0003143549,0.0003652032,0.0006907452,0.0004077718,0.0002177076],"domain_scores_gemma":[0.9962791,0.001063963,0.0004506865,0.0007724917,0.00117574,0.0002580196],"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":[0.00004960907,0.00001174484,0.0007647471,0.0008966621,0.00001973331,0.00001840087,0.00002563355,0.0001156071,0.00008433717,0.0008142492,0.9931495,0.004049864],"study_design_scores_gemma":[0.00006757036,0.000008415215,0.001683463,0.000442926,0.00001472433,0.00004036808,0.00006281209,0.0001075119,0.0001181664,0.001089789,0.996349,0.00001518224],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005759971,0.00008201878,0.00006332755,0.00008119406,0.00002048826,0.00001539817,0.9983538,0.0001568927,0.001169237],"genre_scores_gemma":[0.0002170583,0.00008821687,0.0002485268,0.0001109229,0.000007006721,0.00009562911,0.9982432,0.0000547325,0.0009347567],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1800584,"threshold_uncertainty_score":0.6023552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040237219008637,"score_gpt":0.3464039771206389,"score_spread":0.3360016049305525,"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."}}