{"id":"W4247541456","doi":"10.1109/iris.2017.8250086","title":"IEEE IRIS2017 table of contents","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Table (database); Database","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":[0.0000137608,0.00004263858,0.00007871315,0.00001583859,0.00004495946,0.00001304116,0.0002685864,0.00003483696,0.00002904119],"category_scores_gemma":[0.00004303438,0.00003549849,0.00001181032,0.000009178529,0.00004824606,0.0002251714,0.00003672809,0.00003888612,0.00003387377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004909823,"about_ca_system_score_gemma":0.000001180829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002183818,"about_ca_topic_score_gemma":0.000007237879,"domain_scores_codex":[0.9997709,4.832806e-7,0.00006190711,0.00004656843,0.0000345084,0.00008562158],"domain_scores_gemma":[0.9994796,0.00000511955,0.00002080373,0.0004702509,0.0000130525,0.00001119587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001325726,0.00004831185,0.0180708,0.0001473006,0.0001230531,0.00001521367,0.00005057526,0.002893497,0.5290735,0.01260894,0.2881075,0.1488481],"study_design_scores_gemma":[0.000405252,0.00002954399,0.009663128,0.00002921312,0.000006600627,0.000001872008,0.00006999839,0.005734155,0.9069703,0.001906663,0.07500952,0.0001737594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5477275,0.0004841006,0.1331384,0.0001633259,0.001512405,0.0002054018,0.00009250557,0.001694485,0.3149818],"genre_scores_gemma":[0.9956987,0.0001182596,0.003140013,0.00000436678,0.00001065443,0.000002282903,0.000001440832,0.000006020473,0.001018243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4479712,"threshold_uncertainty_score":0.1447586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03265100993284144,"score_gpt":0.262970993137926,"score_spread":0.2303199832050846,"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."}}