{"id":"W6976866797","doi":"10.60692/hk0kf-nrc03","title":"InductiveQE Datasets","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Disjoint sets; Graph; Graph database; Training set; Set (abstract data type)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002452417,0.0001180647,0.0001210064,0.0002203561,0.0003888047,0.0001561796,0.0008174661,0.00002583446,0.00001673125],"category_scores_gemma":[0.000005143853,0.0001092595,0.00004233228,0.0006209004,0.00001492167,0.002163448,0.000670296,0.0001766991,0.0002593205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001120631,"about_ca_system_score_gemma":0.00002160952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000148197,"about_ca_topic_score_gemma":1.654928e-8,"domain_scores_codex":[0.9987646,0.00009265405,0.0003348911,0.0001759143,0.000401952,0.000229959],"domain_scores_gemma":[0.9989789,0.000006158275,0.0002255863,0.0006768613,0.00004307102,0.00006944843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002400877,0.00002897672,0.09992349,0.0005798487,0.0002738683,0.0002221151,0.4491811,0.133735,0.00001915083,0.2277349,0.03965058,0.04841078],"study_design_scores_gemma":[0.007471761,0.0008600137,0.09330336,0.0001657247,0.00005718462,0.003171344,0.05602137,0.6492607,0.001638322,0.0004672059,0.1839731,0.003609875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06521067,0.000004610169,0.9286541,0.0001696617,0.00165468,0.0004371128,0.0003787832,0.00079926,0.002691132],"genre_scores_gemma":[0.9962916,1.787254e-8,0.003025781,0.000446851,0.00003504963,0.0001048201,0.0000558967,0.000004431721,0.00003560099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9310809,"threshold_uncertainty_score":0.4455475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03093802662762828,"score_gpt":0.2139466647515643,"score_spread":0.183008638123936,"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."}}