{"id":"W4403288974","doi":"10.1088/2515-7620/ad85c5","title":"Projecting future changes in potato yield using machine learning techniques: a case study for Prince Edward Island, Canada","year":2024,"lang":"en","type":"article","venue":"Environmental Research Communications","topic":"Potato Plant Research","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Agriculture; Yield (engineering); Livelihood; Climate change; Food security; Greenhouse gas; Cornerstone; Agricultural productivity; Agricultural engineering; Agricultural economics; Environmental science; Geography; Economics; Engineering; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006805501,0.0005471073,0.0002732773,0.0006646874,0.001490974,0.001316157,0.001316386,0.0006495664,0.001495734],"category_scores_gemma":[0.001494646,0.000240067,0.0005231341,0.002089951,0.0005486243,0.0003743418,0.0005490334,0.0008094882,0.0002001207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02099881,"about_ca_system_score_gemma":0.01839841,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9897205,"about_ca_topic_score_gemma":0.9907286,"domain_scores_codex":[0.9997009,0.00005902888,0.00001532247,0.00004915472,0.00009087416,0.00008460465],"domain_scores_gemma":[0.9991143,0.0002472435,0.00004171343,0.00004467901,0.0004389144,0.0001131532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002802939,0.0002845108,0.2231395,0.0003047414,0.0002631723,0.005623782,0.001202974,0.7078485,0.002595356,0.003988178,0.00934941,0.04511948],"study_design_scores_gemma":[0.00007616205,0.0001187573,0.1638911,0.00009360956,0.00008542208,0.0003062015,0.005773232,0.8141252,0.002300062,0.001279243,0.01182696,0.0001239441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9810711,0.0003208924,0.003864153,0.001214018,0.00002069099,0.000136844,0.004296241,0.0001581801,0.008917917],"genre_scores_gemma":[0.9892644,0.000340131,0.004808641,0.00009280595,0.000004150608,0.00004063259,0.002144304,0.00002375536,0.003281277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02099881,"threshold_uncertainty_score":0.1523578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1104793275211314,"score_gpt":0.3685466853568154,"score_spread":0.258067357835684,"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."}}