{"id":"W7115031693","doi":"","title":"Causal inference and forecasting in the mining industry: Applications of econometric, cointegration, wavelet coherence, bayesian, and machine learning methods","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Causal inference; Inference; Wavelet; Pattern recognition (psychology); Time series","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001558641,0.0003957839,0.0005973706,0.0007268391,0.0003122584,0.00008974706,0.0003252609,0.000678896,0.00003300996],"category_scores_gemma":[0.0008465913,0.0003920447,0.00006218968,0.0007495736,0.00004113492,0.0002643809,0.0000773348,0.001806802,8.546873e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001490567,"about_ca_system_score_gemma":0.00003259242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002978024,"about_ca_topic_score_gemma":0.001692079,"domain_scores_codex":[0.9980873,0.0001922606,0.0008550162,0.0004557289,0.0001154289,0.0002942065],"domain_scores_gemma":[0.9981034,0.00104936,0.0003545595,0.0003067088,0.0001037156,0.00008226597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002706233,0.00003749767,0.002602933,0.0009977377,0.0001303698,0.000004455182,0.0001179825,0.001441204,0.001219696,0.04904615,0.000005244945,0.9443697],"study_design_scores_gemma":[0.005539824,0.00129471,0.03720578,0.008331302,0.001466135,0.0002781084,0.01813079,0.5694164,0.08707183,0.1273518,0.134661,0.009252314],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9453067,0.0008408221,0.0004102131,0.000009922923,0.0001573522,0.0008877684,0.0004146185,0.0001941585,0.05177845],"genre_scores_gemma":[0.9668992,0.0004903043,0.0312801,0.00002659765,0.00001509695,0.0003171703,0.0004079816,0.00006122917,0.00050235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9351174,"threshold_uncertainty_score":0.9998531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0359514462742026,"score_gpt":0.2784647314599534,"score_spread":0.2425132851857508,"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."}}