{"id":"W7047435888","doi":"","title":"Government of Canada private security guard spending","year":2012,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Guard (computer science); Government (linguistics); Private sector; Private security; Key (lock)","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":[0.0007549378,0.001154133,0.001017495,0.004823945,0.006454763,0.006882572,0.001771299,0.002702921,0.3381177],"category_scores_gemma":[0.004293252,0.0009908174,0.001021155,0.006319507,0.001234066,0.001423127,0.001720623,0.002501087,0.1048676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04917446,"about_ca_system_score_gemma":0.13041,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9834163,"about_ca_topic_score_gemma":0.9898093,"domain_scores_codex":[0.9975675,0.0000454697,0.0000388053,0.0001499492,0.001423376,0.00077499],"domain_scores_gemma":[0.9949735,0.0002032062,0.00009346686,0.0002256134,0.003594651,0.0009095717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0000281621,0.00001608843,0.0003610563,0.00005152321,0.000004273496,0.0000244828,0.00005080342,0.00008379312,0.00005109394,0.004554959,0.9822427,0.01253103],"study_design_scores_gemma":[0.00001135814,0.000006077654,0.003497584,0.00007168234,0.000007265978,0.00001533772,0.0001848463,0.0001296133,0.0001234842,0.0005404163,0.9953989,0.00001334838],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.002898975,0.001473766,0.0002223802,0.007969083,0.001183148,0.0001540938,0.06375966,0.0009712919,0.9213675],"genre_scores_gemma":[0.004098252,0.0007485666,0.000144637,0.0006165112,0.00004316594,0.00002582415,0.004483737,0.0001558024,0.9896835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3381177,"threshold_uncertainty_score":0.9440948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004550097579382626,"score_gpt":0.1814107229951148,"score_spread":0.1768606254157322,"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."}}