{"id":"W7019033224","doi":"","title":"Episode 23: Big Tech Energy (with Maya Kosoff)","year":2018,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Censorship; Maya; Government (linguistics); High tech; Joins; Energy (signal processing)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005453413,0.0003582211,0.0002149865,0.0004469734,0.006517912,0.005820321,0.0008623333,0.004621079,0.3894404],"category_scores_gemma":[0.002470545,0.0003043842,0.0002995761,0.0008091138,0.0005085543,0.003017834,0.002972701,0.004462871,0.1520417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002133965,"about_ca_system_score_gemma":0.002775132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06037524,"about_ca_topic_score_gemma":0.1804765,"domain_scores_codex":[0.9991904,0.00005644299,0.00001423063,0.00006417923,0.0003020204,0.0003727985],"domain_scores_gemma":[0.9993298,0.00006539422,0.00002728698,0.00004983948,0.0002132757,0.0003144364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001061562,0.000008862753,0.0001300784,0.00001210275,6.021959e-7,0.00008133722,0.0001584691,0.000007958109,0.00003629236,0.001797872,0.9946419,0.003113958],"study_design_scores_gemma":[0.000002533755,0.000003755878,0.0005547403,0.00001826654,4.675921e-7,0.00002082463,0.0004032917,0.000009951426,0.00004058821,0.0003006276,0.9986419,0.000002983011],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002464756,0.0004436116,0.0002452287,0.01948052,0.002731968,0.0001297632,0.003314031,0.000577797,0.9706124],"genre_scores_gemma":[0.008535257,0.0001593795,0.0000454335,0.008582419,0.0003326099,0.00005658951,0.0009110727,0.0002746175,0.9811026],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3894404,"threshold_uncertainty_score":0.8708891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006289999033322127,"score_gpt":0.1803501848148685,"score_spread":0.1740601857815464,"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."}}