{"id":"W4408412415","doi":"10.1145/3723039","title":"IoT-Based Smart Farming Architecture Using Federated Learning: a Nitrous Oxide Emission Prediction Use Case","year":2025,"lang":"en","type":"article","venue":"ACM Journal on Computing and Sustainable Societies","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Nitrous oxide; Internet of Things; Computer science; Architecture; Agriculture; Environmental science; Embedded system; Chemistry; Ecology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006124483,0.0002115687,0.0002265997,0.00004284137,0.003633605,0.0007283787,0.0001280662,0.000167953,0.000005986062],"category_scores_gemma":[0.0005163736,0.00008696568,0.0001376372,0.0004375088,0.00006139855,0.0001032336,0.0001410067,0.0007546268,3.527987e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174873,"about_ca_system_score_gemma":0.00005394845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003371174,"about_ca_topic_score_gemma":0.00001441498,"domain_scores_codex":[0.9985033,0.000258398,0.0002748676,0.0002602192,0.0002013157,0.0005019272],"domain_scores_gemma":[0.9987759,0.0006158729,0.0001563487,0.00004554165,0.0002833363,0.0001230271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0009541729,0.0008472232,0.3936985,0.0006546158,0.0006264153,0.008034749,0.005514395,0.1077341,0.1785972,0.0005151742,0.047814,0.2550093],"study_design_scores_gemma":[0.005697446,0.004588898,0.1398567,0.003971216,0.0007283975,0.01573768,0.3440694,0.1552393,0.01700112,0.009523947,0.3002981,0.003287902],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970091,0.0003981474,0.0004445803,0.001677564,0.0001438722,0.0001288572,0.000001860195,0.0001078446,0.0000881495],"genre_scores_gemma":[0.9956881,0.00002424568,0.0005673837,0.0007122539,0.0002815137,0.000001152828,0.000009715798,0.000002022002,0.002713657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.338555,"threshold_uncertainty_score":0.9976636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01522836807111228,"score_gpt":0.239911541819547,"score_spread":0.2246831737484347,"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."}}