{"id":"W4389373389","doi":"10.2139/ssrn.4639514","title":"Projecting Future Changes in Potato Yield Using Machine Learning Techniques: A Case Study for Prince Edward Island, Canada","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Potato Plant Research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Yield (engineering); Agricultural economics; Geography; History; Regional science; Artificial intelligence; Machine learning; Computer science; Economics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006228214,0.0004735829,0.0003371078,0.0008436691,0.002034704,0.001638773,0.001282943,0.0007312006,0.001949754],"category_scores_gemma":[0.001774295,0.0002421927,0.00042503,0.00313551,0.0005570217,0.0004462007,0.0005824757,0.0008691231,0.00021577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02545157,"about_ca_system_score_gemma":0.02276067,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946332,"about_ca_topic_score_gemma":0.9966453,"domain_scores_codex":[0.9996896,0.0000475356,0.00001285364,0.00003911164,0.0000985421,0.0001123878],"domain_scores_gemma":[0.999018,0.0002582201,0.0000545115,0.0000385019,0.0004961116,0.000134588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007577866,0.000524373,0.5733309,0.0005417655,0.0004490893,0.01330505,0.00650412,0.22392,0.004584248,0.007264566,0.0228687,0.1459493],"study_design_scores_gemma":[0.0001251377,0.0002642121,0.6669863,0.000235285,0.000224442,0.000965705,0.04115503,0.2469732,0.003460597,0.002231241,0.03719358,0.0001853271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980323,0.000645099,0.002130063,0.001570342,0.00001906873,0.0001063948,0.002841498,0.00008199368,0.0122825],"genre_scores_gemma":[0.9856907,0.0007566725,0.003910313,0.0001160931,0.000006282982,0.00002697469,0.001293032,0.0000332057,0.008166811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02545157,"threshold_uncertainty_score":0.1846649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04517060954559949,"score_gpt":0.3005391729402196,"score_spread":0.2553685633946201,"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."}}