{"id":"W2572951772","doi":"10.1145/3040230.3040239","title":"Search (Non-)Neutrality and Impact on Innovation","year":2017,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Economic Growth and Development","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Neutrality; Business; Political science; Law","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":[0.006173587,0.0001437655,0.000235613,0.0003509394,0.0004224045,0.0003000685,0.001091855,0.00004534094,0.00004548579],"category_scores_gemma":[0.001815378,0.0001136592,0.00003909427,0.0008514916,0.00003447938,0.001134499,0.0003752638,0.0001470446,0.0001647423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000169649,"about_ca_system_score_gemma":0.0003180504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001076911,"about_ca_topic_score_gemma":3.585102e-7,"domain_scores_codex":[0.9984229,0.00007184454,0.0004131011,0.0003567871,0.000510722,0.0002246394],"domain_scores_gemma":[0.9975435,0.0001243861,0.000311887,0.001482379,0.0004614134,0.00007647071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000002922235,0.00001771224,0.07805669,0.0003116153,0.00001145516,3.09478e-7,0.00005145889,0.00004156587,0.000009964832,0.00158385,0.001392077,0.9185204],"study_design_scores_gemma":[0.0003736341,0.0001326438,0.9495566,0.0004643477,0.00001274375,0.000004129738,0.000001247093,0.04697469,0.0006121121,0.0005762517,0.001112043,0.0001795109],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870837,0.004161415,0.002298532,0.002262607,0.0002698905,0.0007615421,0.000001469267,0.00002878856,0.003132086],"genre_scores_gemma":[0.9751076,0.0204936,0.003136627,0.001110047,0.00003722448,0.00005257765,0.00001075538,0.000005184282,0.00004639594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9183409,"threshold_uncertainty_score":0.4634886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1423192492154084,"score_gpt":0.4026222062088647,"score_spread":0.2603029569934563,"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."}}