{"id":"W7134197168","doi":"10.5281/zenodo.18908372","title":"Natural Language Processing Challenges and Opportunities for African Languages in Sierra Leone Context,","year":2010,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sierra leone; Scope (computer science); Languages of Africa; Inclusion (mineral); Process (computing); Resource (disambiguation); Limiting","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":[0.001209986,0.00006824767,0.0001085277,0.000171214,0.001097807,0.0003106209,0.0002817282,0.00003950033,0.0006961501],"category_scores_gemma":[0.0008413161,0.00006794772,0.00002670465,0.0001664179,0.0002370669,0.0002041803,0.0001558758,0.0001653817,0.00002842458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003077763,"about_ca_system_score_gemma":0.00000828979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001031513,"about_ca_topic_score_gemma":0.0001694407,"domain_scores_codex":[0.9990163,0.0002632849,0.000122786,0.0001961638,0.0001984157,0.0002030248],"domain_scores_gemma":[0.9994166,0.0000933325,0.00005956206,0.00008815632,0.0002532407,0.00008908411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001634821,0.00003026059,0.000001784867,0.00002738562,0.000008540903,0.00000553261,0.0291919,0.000001056265,0.0008574674,0.02368741,0.000833356,0.945339],"study_design_scores_gemma":[0.0003227862,0.00004659581,0.001509586,0.00001978866,0.00001048529,0.00000953865,0.07788686,0.0008345367,0.000047999,0.0008328674,0.9183389,0.000140065],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4599649,0.01374111,0.002734263,0.02993429,0.0002936854,0.001260584,0.0001285739,0.001143101,0.4907995],"genre_scores_gemma":[0.9972005,0.0002346311,0.0006759288,0.00008437227,0.0001561006,9.606095e-8,0.0001073186,0.0001461142,0.001394887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9451989,"threshold_uncertainty_score":0.8443558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09816612119781166,"score_gpt":0.3440154371553577,"score_spread":0.2458493159575461,"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."}}