{"id":"W4229744278","doi":"10.1007/978-1-4614-6170-8_100326","title":"Edge Prediction","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Computational Physics and Python Applications","field":"Computer Science","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":[0.0003767191,0.001216372,0.0007628019,0.001084234,0.0006551941,0.001563937,0.002305101,0.001057117,0.05874559],"category_scores_gemma":[0.001381527,0.0004221047,0.0007558474,0.001191319,0.0003382097,0.002275648,0.001307857,0.001880896,0.03612023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004475343,"about_ca_system_score_gemma":0.0006297817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003268958,"about_ca_topic_score_gemma":0.003989774,"domain_scores_codex":[0.9997601,0.0000142034,0.000005525957,0.00007669125,0.000109976,0.00003351677],"domain_scores_gemma":[0.9996089,0.00007418096,0.00001946779,0.0001342016,0.0001285901,0.00003466132],"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":[0.0001704217,0.0001002551,0.000680497,0.0002277141,0.00004046013,0.00008451349,0.00003102224,0.03428593,0.006661282,0.04344378,0.259237,0.6550371],"study_design_scores_gemma":[0.0000350287,0.00007477258,0.001277187,0.0002110034,0.00007412125,0.0003206463,0.00007162939,0.5474514,0.03129358,0.1248216,0.2942913,0.00007773606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01034565,0.00361064,0.7052906,0.001462914,0.002184794,0.00019235,0.004979122,0.01817529,0.2537586],"genre_scores_gemma":[0.1779202,0.005509143,0.399494,0.001711954,0.001110658,0.0002571169,0.02040753,0.006317411,0.3872719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05874559,"threshold_uncertainty_score":0.1965236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171889648667852,"score_gpt":0.2262386861540198,"score_spread":0.2090497212872346,"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."}}