{"id":"W7134916319","doi":"10.5281/zenodo.18956032","title":"Natural Language Processing for African Languages in Zambia: Challenges and Opportunities","year":2012,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"ICT in Developing Communities","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Scarcity; Component (thermodynamics); Natural language; Scalability; Languages of Africa; Scripting language; Service (business)","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":[0.005162733,0.0004063358,0.0006101769,0.006783632,0.001233288,0.003067968,0.0007171038,0.0008127948,0.00275051],"category_scores_gemma":[0.0243993,0.0003141383,0.0005842736,0.00861899,0.0008737964,0.002690217,0.001395125,0.0007057714,0.0002157457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002822449,"about_ca_system_score_gemma":0.01074322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07767989,"about_ca_topic_score_gemma":0.1529512,"domain_scores_codex":[0.9978313,0.001365615,0.0002599276,0.0001803874,0.0001873599,0.0001754807],"domain_scores_gemma":[0.98883,0.009488372,0.0007754078,0.0002026039,0.000599159,0.0001044618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006975825,0.0002739161,0.1369228,0.06447975,0.0005575513,0.008256196,0.03541862,0.01129199,0.009245173,0.02075315,0.007826798,0.7042765],"study_design_scores_gemma":[0.0003263444,0.0005617771,0.4787052,0.07195822,0.002698355,0.005445732,0.1759181,0.02670199,0.008961823,0.01986351,0.208601,0.0002579546],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7459776,0.1904274,0.0162196,0.02090838,0.0001212572,0.001611933,0.009503837,0.0002043587,0.01502563],"genre_scores_gemma":[0.9141747,0.0605554,0.02025457,0.0007131936,0.00003939912,0.0008586324,0.002608438,0.00002881012,0.0007668661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07767989,"threshold_uncertainty_score":0.1544555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08783770769593668,"score_gpt":0.2815220118898313,"score_spread":0.1936843041938946,"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."}}