{"id":"W4304128274","doi":"10.3390/agriculture12101657","title":"Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms","year":2022,"lang":"en","type":"article","venue":"Agriculture","topic":"Tree Root and Stability Studies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island; Dalhousie University","funders":"","keywords":"Correlation coefficient; Mean squared error; Coefficient of determination; Mathematics; Vaccinium; Linear regression; Yield (engineering); Regression analysis; Horticulture; Environmental science; Algorithm; Statistics; Biology; 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":[0.001240541,0.0007993307,0.0004766338,0.0009230302,0.0002116029,0.000400578,0.0005558748,0.0005023307,0.0004130575],"category_scores_gemma":[0.002138468,0.0001892302,0.0004950815,0.0005034997,0.0001655314,0.0003357999,0.0003002117,0.000382622,0.0001246308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006179367,"about_ca_system_score_gemma":0.0007468303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01848113,"about_ca_topic_score_gemma":0.01185073,"domain_scores_codex":[0.9997445,0.00006072297,0.00002796246,0.00007659649,0.00004204613,0.00004815976],"domain_scores_gemma":[0.9987611,0.0007351885,0.00015448,0.00007116785,0.0002360664,0.00004204208],"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":[0.0002430178,0.0002252626,0.03217041,0.00003057412,0.00006784038,0.00006762263,0.0000282746,0.894591,0.001928882,0.0001636088,0.0005106657,0.06997287],"study_design_scores_gemma":[0.000003410557,0.00002823039,0.002852167,0.000001873606,0.000004206755,0.000005163183,0.000005471991,0.9966692,0.0003448671,0.00005519309,0.00002815839,0.000002041538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9639951,0.0002913598,0.03428574,0.00006804943,0.00001252598,0.000034525,0.0002381237,0.0003404607,0.0007341296],"genre_scores_gemma":[0.9891694,0.00005677867,0.009940404,0.00001603926,0.000006626256,0.00003082764,0.0003805229,0.00000629469,0.0003932469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01848113,"threshold_uncertainty_score":0.0367471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005214992844314699,"score_gpt":0.169621705266207,"score_spread":0.1644067124218923,"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."}}