{"id":"W4229619286","doi":"10.1515/iupac.88.1339","title":"Somatic","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Neurology and Historical Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Computer science; Linguistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001966939,0.0003416345,0.000599932,0.0001079652,0.0008646307,0.00007164938,0.0009184685,0.0002823633,0.0009766773],"category_scores_gemma":[0.004737146,0.000283579,0.0001468279,0.00007528128,0.0004986192,0.0000767426,0.0003508175,0.000756063,0.00003015566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001677098,"about_ca_system_score_gemma":0.0002325602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005001128,"about_ca_topic_score_gemma":0.000373677,"domain_scores_codex":[0.9978066,0.0001221144,0.000300053,0.0006169875,0.0007554734,0.0003987771],"domain_scores_gemma":[0.998184,0.0002651637,0.0003358671,0.001016062,0.00008322246,0.0001156508],"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":[0.00005928502,0.0001623123,0.000003196711,0.0000784259,0.00001187749,0.000341478,0.000007746949,2.999114e-7,0.00007258216,0.00001374518,0.998772,0.0004770719],"study_design_scores_gemma":[0.000326175,0.0001988012,0.0000296525,0.00005011165,0.0000738243,0.00002950846,0.000001324668,0.000001903647,0.0001324497,0.0005578633,0.9983256,0.0002727268],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001235258,0.0004591951,0.000006224362,0.001010668,0.002086282,0.0001828541,0.9957117,0.00007649133,0.0003430263],"genre_scores_gemma":[0.0001336985,0.001258732,0.000006377302,0.001285282,0.0006849457,0.00001799138,0.9952568,0.00002451356,0.001331647],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.004540452,"threshold_uncertainty_score":0.9999616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04096715074001877,"score_gpt":0.4423895572316823,"score_spread":0.4014224064916635,"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."}}