{"id":"W4406774861","doi":"10.3389/frai.2024.1496066","title":"Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision","year":2025,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Advanced Cyberinfrastructure; Division of Graduate Education; Hatch; Ohio Soybean Council; Ohio State University; Nationwide; National Science Foundation","keywords":"Cyberinfrastructure; Computer science; Machine learning; Multispectral image; Agriculture; Terabyte; Precision agriculture; Artificial intelligence; Field (mathematics); Big data; Data science; Software; Data processing; Data mining; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001682518,0.0001388761,0.0002420279,0.00008747173,0.0001811049,0.00007843135,0.0001974693,0.0001177101,0.00002912428],"category_scores_gemma":[0.00005308746,0.00005621701,0.00007830751,0.002158673,0.00007196431,0.00009681845,0.00005051233,0.0001712633,9.025769e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003290129,"about_ca_system_score_gemma":0.000005053845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003158772,"about_ca_topic_score_gemma":0.005789159,"domain_scores_codex":[0.9989526,0.00004432218,0.0003056286,0.0003777161,0.00009077526,0.0002289733],"domain_scores_gemma":[0.9996952,0.0001191135,0.00006024999,0.00004371397,0.00003770998,0.00004399755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007728081,0.0001654075,0.5049898,0.00001780747,0.00005984573,0.000001823468,0.001811826,0.001220375,0.0220105,0.004399837,0.001231862,0.4640136],"study_design_scores_gemma":[0.0001207296,0.0003005515,0.7327425,0.0001015529,0.0001691225,0.000001783675,0.04778736,0.02117513,0.05027151,0.0431277,0.1033115,0.0008905175],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9394204,0.002081847,0.0560402,0.0009267643,0.0003395363,0.0007947453,0.0000211923,0.00005276333,0.0003225809],"genre_scores_gemma":[0.9969614,0.000137568,0.002217267,0.00008274926,0.00007634344,0.0002167938,0.0001228819,5.664573e-7,0.0001845027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4631231,"threshold_uncertainty_score":0.3230487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008902901922409043,"score_gpt":0.2600343643219574,"score_spread":0.2511314623995484,"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."}}