{"id":"W4251974097","doi":"10.1515/iupac.88.0988","title":"Linkage Analysis","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Effects and risks of endocrine disrupting chemicals","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Data science; Linguistics; Data mining; Philosophy","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01317256,0.001837338,0.00309111,0.01020622,0.002228273,0.003712459,0.004052555,0.002616825,0.15708],"category_scores_gemma":[0.1051935,0.0008975916,0.004123761,0.01546307,0.0005220158,0.002495485,0.003594603,0.002855805,0.03846293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002146687,"about_ca_system_score_gemma":0.008422844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156084,"about_ca_topic_score_gemma":0.015423,"domain_scores_codex":[0.9795263,0.006745597,0.003385008,0.007324973,0.0021623,0.0008556915],"domain_scores_gemma":[0.9646444,0.0202799,0.002323642,0.006781581,0.005546892,0.0004236129],"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":[0.0008996965,0.0001531102,0.01991848,0.008163236,0.002823693,0.0002830467,0.00036637,0.002375286,0.0002689313,0.01046013,0.8540579,0.1002301],"study_design_scores_gemma":[0.001052888,0.0001602153,0.01391803,0.002997366,0.002641535,0.0006503382,0.0006367537,0.004974681,0.0007856803,0.03579952,0.936201,0.0001821116],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005000709,0.00231062,0.0289407,0.001718418,0.0006347653,0.001809066,0.9417505,0.003463555,0.0143717],"genre_scores_gemma":[0.03247117,0.001889676,0.06333321,0.00165762,0.0003167121,0.01640997,0.8697289,0.001791635,0.01240113],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8429199,"threshold_uncertainty_score":0.525485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103217076404337,"score_gpt":0.4606395560919309,"score_spread":0.4496073853278875,"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."}}