{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003102395,0.0006366531,0.000249803,0.0008213092,0.0006838764,0.003860396,0.001011357,0.001081765,0.003882613],"category_scores_gemma":[0.004944414,0.0002753245,0.0003096466,0.001417345,0.001943734,0.005855268,0.001669476,0.001836199,0.001692052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075721,"about_ca_system_score_gemma":0.0009576922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002151871,"about_ca_topic_score_gemma":0.001919503,"domain_scores_codex":[0.9988919,0.0004030558,0.00005368611,0.0001739719,0.0003934267,0.00008401467],"domain_scores_gemma":[0.9966199,0.001876936,0.0001271171,0.0003729909,0.0007202678,0.0002827176],"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":[0.0001168496,0.0003105867,0.009817702,0.0006445778,0.00003194828,0.0002601292,0.003637339,0.00723874,0.00673555,0.04645562,0.07218242,0.8525686],"study_design_scores_gemma":[0.00002438707,0.0004268458,0.01403293,0.001172006,0.00002347039,0.0009306846,0.004026044,0.0409637,0.0135087,0.09203624,0.8327351,0.0001198964],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1571826,0.08147439,0.5343192,0.07539411,0.001552524,0.0003803177,0.001248791,0.002579575,0.1458684],"genre_scores_gemma":[0.5787591,0.07151379,0.3160603,0.003848703,0.001835175,0.000294895,0.001796107,0.0004725505,0.02541948],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.003882613,"threshold_uncertainty_score":0.01640719,"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."}}