{"id":"W4414663776","doi":"10.1057/s41599-025-05849-x","title":"Investigating the nonlinear nexus between natural resources, digitization, economic policy uncertainty, and financial structure in Canada","year":2025,"lang":"en","type":"article","venue":"Humanities and Social Sciences Communications","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Natural resource; Nexus (standard); Quantile regression; Resource (disambiguation); Leverage (statistics); Financial sector development; Resource curse; Digitization; Quality (philosophy)","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003272392,0.00007374114,0.0001577767,0.00009847841,0.001528162,0.0002213555,0.0004407935,0.0000307698,0.00001026993],"category_scores_gemma":[0.0001115066,0.0000697849,0.0000161445,0.0002167515,0.0007345182,0.0001419115,0.000277941,0.0001698952,1.464778e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002385559,"about_ca_system_score_gemma":0.0004518087,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7267358,"about_ca_topic_score_gemma":0.9463655,"domain_scores_codex":[0.9993274,0.00004423824,0.0002978039,0.0001539162,0.00002560337,0.0001510413],"domain_scores_gemma":[0.9994366,0.0002271118,0.0001225923,0.0001843639,0.00001625046,0.00001304805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[4.459815e-7,0.000002196627,0.3118615,0.000008805609,0.000004393937,2.158755e-8,0.002930185,0.00002534561,9.509463e-8,0.6834159,0.0001298828,0.001621152],"study_design_scores_gemma":[0.0001476369,0.00000646402,0.6382199,0.00001364189,0.000003343527,2.326584e-7,0.003325741,0.06572574,1.123275e-7,0.2181386,0.07428219,0.000136382],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738457,0.001186004,0.00000788068,0.009043901,0.00007548881,0.0001182474,0.0003587175,0.000006661275,0.01535744],"genre_scores_gemma":[0.9985123,0.0001858695,0.00009193429,0.0009948864,0.00006527104,0.000008168305,0.00002958952,0.000002595241,0.0001093749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4652773,"threshold_uncertainty_score":0.9997717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04347011570168535,"score_gpt":0.2594503670743647,"score_spread":0.2159802513726793,"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."}}