{"id":"W3174487281","doi":"10.3390/su13137366","title":"Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Political Conflict and Governance","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"United States Agency for International Development","keywords":"Machine learning; Artificial intelligence; Interpretability; Boosting (machine learning); Computer science; Metric (unit); Resampling; Decision tree; Gradient boosting; Multilayer perceptron; Data mining; Random forest; Artificial neural network; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001179348,0.0001190693,0.0001954159,0.00005126994,0.0002715194,0.00007867825,0.0001613077,0.000133604,0.0001401354],"category_scores_gemma":[0.002085108,0.0001261521,0.00009914731,0.0006123509,0.0001514743,0.000231905,0.00008403255,0.0003307185,0.00001827657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000848604,"about_ca_system_score_gemma":0.000557815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003026239,"about_ca_topic_score_gemma":0.000727694,"domain_scores_codex":[0.9977629,0.0005824502,0.0002637136,0.0004213884,0.0004020625,0.0005674488],"domain_scores_gemma":[0.9990441,0.00009029552,0.00005553461,0.0002365357,0.0003841524,0.0001894096],"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":[0.0001876884,0.001343568,0.79571,0.0004362943,0.00004832272,0.00003732057,0.07612976,0.0001359314,0.0002897957,0.09595887,0.008062501,0.02165999],"study_design_scores_gemma":[0.0002817083,0.00004821549,0.2968934,0.00001562574,0.00001057725,5.352053e-7,0.001336403,0.0003077589,0.0002667216,0.01174774,0.6889372,0.0001541187],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.885612,0.001029518,0.00198884,0.01637925,0.0001932091,0.00113639,0.00005274026,0.0001605336,0.09344759],"genre_scores_gemma":[0.9834512,0.0000575467,0.00009733692,0.0003343176,0.000148411,0.00007425665,0.00001955708,0.000009377813,0.01580796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6808747,"threshold_uncertainty_score":0.5144332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02626818971647041,"score_gpt":0.3069881511633457,"score_spread":0.2807199614468753,"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."}}