{"id":"W6922662619","doi":"10.13140/rg.2.2.22202.95680","title":"Falling short: suboptimal outcomes in Canadian defence procurement","year":2018,"lang":"en","type":"article","venue":"","topic":"Educational Robotics and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Falling (accident); Government (linguistics); Procurement; Investment (military)","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":[0.01036024,0.0004137943,0.0007625344,0.003327946,0.01080077,0.008814478,0.003121668,0.003606556,0.01682924],"category_scores_gemma":[0.04057435,0.0004152552,0.0007355897,0.0103831,0.004798718,0.002302356,0.003819636,0.004234403,0.0009499477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09881776,"about_ca_system_score_gemma":0.1397537,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9758973,"about_ca_topic_score_gemma":0.9901647,"domain_scores_codex":[0.9845456,0.002222519,0.0004166866,0.0006063185,0.004005608,0.008203321],"domain_scores_gemma":[0.9609211,0.007716505,0.005364288,0.001296856,0.01268811,0.01201309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009046476,0.0003157049,0.7045438,0.0002575421,0.000218918,0.001247669,0.009150002,0.007214217,0.0001759761,0.06099935,0.1074211,0.1075511],"study_design_scores_gemma":[0.00009498354,0.0001503316,0.8736696,0.0007140539,0.0001434983,0.0005739505,0.05355303,0.004685474,0.0003387461,0.0255003,0.04033916,0.0002368271],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8788071,0.004745407,0.0009376784,0.04291639,0.0003252297,0.00008537906,0.003074507,0.00006320869,0.0690451],"genre_scores_gemma":[0.9930407,0.000815224,0.0001628081,0.001419691,0.00003142804,0.00001547959,0.0004658752,0.00002558746,0.004023203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09881776,"threshold_uncertainty_score":0.7169764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02411591602718466,"score_gpt":0.2626559237937894,"score_spread":0.2385400077666048,"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."}}