{"id":"W7058483848","doi":"","title":"Nordic markets 3rd party data center and hyperscale investment tops USD 3 billion","year":2017,"lang":"en","type":"other","venue":"","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"TOPS; Investment (military); Center (category theory); Work (physics); Quarter (Canadian coin)","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":[],"consensus_categories":[],"category_scores_codex":[0.001111114,0.001021778,0.0005409832,0.001862142,0.001879361,0.006058998,0.001059756,0.00188318,0.2033112],"category_scores_gemma":[0.002512374,0.0004476979,0.0006852294,0.001291341,0.0008457316,0.002785234,0.001866583,0.002786479,0.1026899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003610566,"about_ca_system_score_gemma":0.008258044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03962785,"about_ca_topic_score_gemma":0.04611005,"domain_scores_codex":[0.9991443,0.00004047091,0.00001398824,0.00009799096,0.000488222,0.0002150266],"domain_scores_gemma":[0.9988526,0.00007332697,0.0000556267,0.000106168,0.0005093917,0.0004028928],"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":[0.0001643498,0.0000498935,0.0003599016,0.00003895037,0.000006061116,0.00004685979,0.00001552876,0.0003714511,0.0003152828,0.0185922,0.960184,0.01985557],"study_design_scores_gemma":[0.00004501683,0.00002659161,0.0009613596,0.00005944846,0.000004652195,0.00002940553,0.00005952573,0.0009639035,0.0008137253,0.005164694,0.9918587,0.00001302214],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.005563322,0.001132137,0.00162623,0.004603159,0.002130949,0.00008478839,0.0285213,0.002556287,0.9537818],"genre_scores_gemma":[0.02394036,0.001061623,0.001645648,0.00104015,0.0003936983,0.00006861999,0.02341186,0.001428831,0.9470093],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2033112,"threshold_uncertainty_score":0.6801436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03598421623174958,"score_gpt":0.2909560813782726,"score_spread":0.254971865146523,"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."}}