{"id":"W2234043343","doi":"10.1177/2372732215602907","title":"Accuracy of Intelligence Forecasts From the Intelligence Consumer’s Perspective","year":2015,"lang":"en","type":"article","venue":"Policy Insights from the Behavioral and Brain Sciences","topic":"Competitive and Knowledge Intelligence","field":"Business, Management and Accounting","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Defence Research and Development Canada","funders":"Defence Research and Development Canada","keywords":"Perspective (graphical); Meaning (existential); Context (archaeology); Psychology; Intelligence analysis; Term (time); Social psychology; Cognitive psychology; Artificial intelligence; Computer science; Econometrics; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050693,0.0002858816,0.0002870319,0.001217163,0.0004250122,0.003525036,0.0003323073,0.0009880437,0.002848275],"category_scores_gemma":[0.05626561,0.0002146848,0.0004040782,0.0007407163,0.001219759,0.003486345,0.001037152,0.001303089,0.0005444546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009567505,"about_ca_system_score_gemma":0.0004781044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005060202,"about_ca_topic_score_gemma":0.003133924,"domain_scores_codex":[0.9954526,0.001178107,0.0003134253,0.0004907022,0.002398998,0.0001662316],"domain_scores_gemma":[0.9703677,0.01491436,0.005401671,0.002816652,0.0059427,0.000556985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002524774,0.0003144699,0.7625888,0.000265206,0.0005283116,0.0006390517,0.03024065,0.01009146,0.01617156,0.02994545,0.005644828,0.1410455],"study_design_scores_gemma":[0.00008532893,0.0007381537,0.8976001,0.0001796297,0.0003098248,0.0006578678,0.01411061,0.03124692,0.01148787,0.03274201,0.01053241,0.000309207],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618257,0.0002261063,0.004844522,0.001086691,0.00005510388,0.00003110648,0.0003119809,0.00006579931,0.03155285],"genre_scores_gemma":[0.9980736,0.00008469254,0.001070647,0.00007965603,0.0000212385,0.000007615985,0.0001460772,0.00001208209,0.0005043215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0050693,"threshold_uncertainty_score":0.02680933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1689626563433129,"score_gpt":0.3678857346436298,"score_spread":0.1989230783003169,"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."}}