{"id":"W4408196902","doi":"10.1038/s44264-025-00052-6","title":"Advanced machine learning for regional potato yield prediction: analysis of essential drivers","year":2025,"lang":"en","type":"article","venue":"npj Sustainable Agriculture","topic":"Potato Plant Research","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency","keywords":"Yield (engineering); Machine learning; Artificial intelligence; Agricultural engineering; Computer science; Engineering; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002346337,0.0001592937,0.0003157322,0.0000923512,0.0003970876,0.00005428188,0.000297335,0.000161776,0.0002032402],"category_scores_gemma":[0.0002398012,0.00006090317,0.0002827556,0.002490886,0.00005858689,0.0001692916,0.0001051057,0.0002346685,0.000001437955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008207591,"about_ca_system_score_gemma":0.00003407428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005301131,"about_ca_topic_score_gemma":0.0006178444,"domain_scores_codex":[0.9985509,0.00006166856,0.0002458976,0.0003664208,0.000312422,0.000462704],"domain_scores_gemma":[0.9987816,0.0004120549,0.000115631,0.00006152171,0.0005447097,0.00008451052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00106477,0.0006844465,0.1231355,0.0004517679,0.003174015,0.00008585064,0.0007932421,0.01890027,0.7121436,0.03187325,0.08067276,0.02702053],"study_design_scores_gemma":[0.0009985788,0.000701594,0.4842394,0.0001184193,0.001329574,0.000009831691,0.01713703,0.004925574,0.0205902,0.001194457,0.4681957,0.0005596553],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919692,0.0006077474,0.0001982592,0.003036827,0.00006744011,0.0007169982,0.0001773901,0.0001093671,0.003116725],"genre_scores_gemma":[0.9697955,0.000119638,0.00008397923,0.00009856818,0.00008754168,0.00007515529,0.001059404,8.013024e-7,0.02867946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6915534,"threshold_uncertainty_score":0.3054117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00850031122509844,"score_gpt":0.2347108047207194,"score_spread":0.2262104934956209,"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."}}