{"id":"W7127141263","doi":"10.5281/zenodo.18461737","title":"Military Spending On Natural Resource Extraction Projects","year":2025,"lang":"","type":"article","venue":"Open MIND","topic":"Defense, Military, and Policy Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government spending; Investment (military); Government (linguistics); Capital expenditure; Natural resource; Production (economics); Capital investment; Consumption (sociology)","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":[0.0003847021,0.0003046354,0.000318845,0.0008091773,0.001128407,0.001332309,0.0006338992,0.0004120814,0.008981536],"category_scores_gemma":[0.002335875,0.0001750824,0.0004275668,0.001816106,0.0007527192,0.0005635375,0.000511612,0.0005199993,0.0004096196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0168494,"about_ca_system_score_gemma":0.01936203,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9557822,"about_ca_topic_score_gemma":0.9695863,"domain_scores_codex":[0.999459,0.00007605939,0.00001149142,0.00003287656,0.0001485472,0.0002720404],"domain_scores_gemma":[0.9994517,0.0001364478,0.00009332594,0.00002933474,0.0001648457,0.0001243173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000330975,0.0001518352,0.1206865,0.0002099184,0.0001361821,0.0004841265,0.0008177107,0.6719077,0.001552943,0.1324682,0.0243649,0.04688896],"study_design_scores_gemma":[0.0001849589,0.000334823,0.3432955,0.0003060484,0.0002848368,0.0004230159,0.004483379,0.4698026,0.00240907,0.02463177,0.1536523,0.0001915911],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8636665,0.000895981,0.003335652,0.002671559,0.00005604249,0.00007767643,0.009330057,0.0001428299,0.1198238],"genre_scores_gemma":[0.9737299,0.001214974,0.0006570194,0.00007166335,0.00001171119,0.0000217967,0.002256023,0.00001760256,0.02201921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9557822,"threshold_uncertainty_score":0.1222515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.079678314818825,"score_gpt":0.3266099544300494,"score_spread":0.2469316396112244,"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."}}