{"id":"W4239495869","doi":"10.1007/978-1-4939-7131-2_100308","title":"Edge Prediction","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Computer science; Artificial intelligence","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":[0.0003143394,0.001232566,0.0006660815,0.0007458659,0.0004210715,0.001584164,0.001574927,0.0008669994,0.04374582],"category_scores_gemma":[0.001245269,0.0003252749,0.000568629,0.001027505,0.0002217597,0.002194555,0.0009561422,0.001541671,0.0251829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003756249,"about_ca_system_score_gemma":0.0005114102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004555495,"about_ca_topic_score_gemma":0.005157254,"domain_scores_codex":[0.9997863,0.00001355207,0.00000515871,0.00007867313,0.00008846208,0.00002793391],"domain_scores_gemma":[0.9996454,0.00008118575,0.00001452265,0.0001103316,0.000126029,0.00002248004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002028609,0.00009487911,0.0007743079,0.0001486392,0.00003430995,0.00008199763,0.00002812262,0.04583877,0.004555986,0.02314188,0.2338683,0.6912299],"study_design_scores_gemma":[0.00002686049,0.00006763359,0.001359127,0.0001486551,0.00006483036,0.0001990034,0.00005405715,0.6207776,0.01690057,0.06921644,0.2911181,0.00006719519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01211023,0.004609955,0.6485651,0.001900423,0.00360314,0.0001556056,0.006404593,0.01674021,0.3059108],"genre_scores_gemma":[0.2198028,0.007882917,0.2854933,0.002001657,0.001900345,0.0002165482,0.02449551,0.004552938,0.453654],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04374582,"threshold_uncertainty_score":0.1463443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383410875346004,"score_gpt":0.1818829619925178,"score_spread":0.1680488532390577,"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."}}