{"id":"W4408541545","doi":"10.1016/j.atech.2025.100896","title":"A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools","year":2025,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Potato Plant Research","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Health PEI; University of Guelph; University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Agricultural engineering; Crop coefficient; Crop; State (computer science); Computer science; Engineering; Agronomy; Programming language; Biology","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":[0.0006224989,0.001215398,0.0007777124,0.001154269,0.0002857549,0.0007633556,0.001236718,0.0008494346,0.0008937441],"category_scores_gemma":[0.001004448,0.0003737001,0.0007227786,0.001111151,0.0002694725,0.0009067756,0.0004272928,0.0008061097,0.0006172018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004280795,"about_ca_system_score_gemma":0.0008304391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009067132,"about_ca_topic_score_gemma":0.009410451,"domain_scores_codex":[0.9996819,0.00004682497,0.0000207934,0.0001255459,0.00009427959,0.00003072532],"domain_scores_gemma":[0.9994449,0.0002251029,0.00008655026,0.00004615649,0.0001754856,0.0000218367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001590639,0.0003617013,0.02287584,0.0005181767,0.0003461596,0.0001256886,0.00008766032,0.3505562,0.01332005,0.001396731,0.002930876,0.6073219],"study_design_scores_gemma":[0.000005787184,0.00008764556,0.004268368,0.00002703573,0.00004263563,0.00005671422,0.00002187454,0.9895242,0.003636413,0.0006134604,0.001697084,0.00001884024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1164194,0.005206265,0.8705255,0.0003629325,0.0001523059,0.00008702614,0.0005808078,0.002326207,0.004339614],"genre_scores_gemma":[0.8160703,0.002640818,0.1746034,0.000229588,0.000134267,0.0001438021,0.001174749,0.0001042862,0.004898882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009067132,"threshold_uncertainty_score":0.01802868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01175016893144751,"score_gpt":0.2278362965002009,"score_spread":0.2160861275687534,"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."}}