{"id":"W4200566918","doi":"10.1109/iemcon53756.2021.9623142","title":"Masa: AI-Adaptive Mobile App for Sustainable Agriculture","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","topic":"ICT in Developing Communities","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Agriculture; Sustainability; Business; Product (mathematics); Resource (disambiguation); Sustainable agriculture; Marketing; Computer science; Geography","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":[0.0005514072,0.0003185449,0.0003785131,0.0003830434,0.0009205511,0.0006510657,0.001952909,0.0004234107,0.0000345867],"category_scores_gemma":[0.0001841954,0.0003218257,0.0000842641,0.001133783,0.000288698,0.003438462,0.001094332,0.0008514645,0.00003148891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002330195,"about_ca_system_score_gemma":0.001093669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003658766,"about_ca_topic_score_gemma":0.0000701528,"domain_scores_codex":[0.9980007,0.0001220687,0.0006315998,0.0003057878,0.000230544,0.0007092634],"domain_scores_gemma":[0.9940978,0.0002246234,0.0003761444,0.001664213,0.003543412,0.00009385885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001545359,0.00009242266,0.00001690179,0.00008930796,0.00007594202,0.00000172735,0.003994684,0.000289467,0.0002906302,0.8926205,0.00896633,0.09354661],"study_design_scores_gemma":[0.0009660678,0.0007888934,0.00003331907,0.00009717059,0.0000281564,0.00008748831,0.03292314,0.01760999,0.03456909,0.05876027,0.8534328,0.0007036511],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02633524,0.01328504,0.9365685,0.01121747,0.0003293937,0.003482207,0.0001457492,0.001302748,0.007333628],"genre_scores_gemma":[0.9517549,0.008982829,0.03161915,0.001194058,0.00003249785,0.003217871,0.0005081171,0.00002139012,0.002669249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9254196,"threshold_uncertainty_score":0.9999234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007831084705595041,"score_gpt":0.2416955672854232,"score_spread":0.2338644825798282,"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."}}