{"id":"W4386735376","doi":"10.1007/s00521-023-08961-8","title":"QuanCro: a novel framework for quantification of corn crops’ consistency under natural field conditions","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Consistency (knowledge bases); Crop; Computer science; Population; Probabilistic logic; Field (mathematics); Agricultural engineering; Segmentation; Crop yield; Pyramid (geometry); Statistics; Artificial intelligence; Mathematics; Agronomy; Engineering; Forestry; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002125482,0.0008153427,0.0008895513,0.002302756,0.0007350094,0.002069589,0.002239362,0.0009624458,0.002780691],"category_scores_gemma":[0.007432505,0.0005012698,0.0006898413,0.001725775,0.001167488,0.003039381,0.002253456,0.001023405,0.0003801068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001154403,"about_ca_system_score_gemma":0.001412784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01743194,"about_ca_topic_score_gemma":0.02382745,"domain_scores_codex":[0.9987527,0.0002483614,0.00007320002,0.0004840448,0.0003359205,0.0001058305],"domain_scores_gemma":[0.9977156,0.0008298155,0.0004211092,0.0004636802,0.000448964,0.000120753],"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":[0.0009085247,0.0002982257,0.02224263,0.0005933353,0.0003668593,0.0002905306,0.0006518138,0.3444342,0.0503859,0.1434044,0.008318106,0.4281056],"study_design_scores_gemma":[0.00001458575,0.00004735699,0.00477324,0.00002121773,0.00002635283,0.00007385635,0.00006638908,0.9516585,0.005548737,0.03300818,0.004714554,0.00004711733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01454717,0.00020831,0.981039,0.00007815201,0.0000426538,0.00005632042,0.0007134817,0.001537609,0.001777228],"genre_scores_gemma":[0.428005,0.0001871637,0.5667148,0.0001402263,0.00009278439,0.0002605165,0.001412685,0.0005336421,0.002653173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01743194,"threshold_uncertainty_score":0.03466094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04548123733309535,"score_gpt":0.3025534206726476,"score_spread":0.2570721833395523,"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."}}