{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005751749,0.0006263932,0.0008081945,0.002423966,0.0006431086,0.001560674,0.0008536828,0.0009539366,0.0009665383],"category_scores_gemma":[0.01702173,0.0002154316,0.0005064703,0.001728407,0.0006264565,0.001064794,0.0008222109,0.001946945,0.0001408619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011431,"about_ca_system_score_gemma":0.001234255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006617355,"about_ca_topic_score_gemma":0.006308336,"domain_scores_codex":[0.9981691,0.001420013,0.00007049581,0.0001574593,0.0001002041,0.00008285297],"domain_scores_gemma":[0.987857,0.01092298,0.0004700332,0.0001646809,0.000420304,0.0001650022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002956284,0.0003177407,0.1208424,0.0002060393,0.0004097013,0.000329178,0.0004189012,0.6670734,0.0003085516,0.008569806,0.003561983,0.1976667],"study_design_scores_gemma":[0.00001535287,0.00004802637,0.005092546,0.00004820555,0.00002536299,0.00003783614,0.0001582694,0.9814934,0.0001526617,0.01236359,0.0005560195,0.000008741356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6719913,0.005149491,0.3027972,0.01325671,0.0002183648,0.000249041,0.0009408556,0.0005598941,0.004837141],"genre_scores_gemma":[0.9672976,0.0004133657,0.03117222,0.0002131043,0.0001124416,0.000074461,0.0002351807,0.00001330658,0.0004682725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006617355,"threshold_uncertainty_score":0.03041852,"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."}}