{"id":"W6904677755","doi":"10.1371/journal.pone.0260946.s001","title":"Data and coefficients used in the Prairie Crop Energy Model (PCEM).","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Bioenergy crop production and management","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Arable land; Crop; Energy crop; Nitrogen; Measure (data warehouse); Crop yield; Standing crop; Crop coefficient","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001036774,0.0007860411,0.0005513453,0.0008581026,0.0005256156,0.0008335873,0.002045863,0.0005231418,0.06788327],"category_scores_gemma":[0.003278567,0.0004411034,0.000574125,0.002731693,0.0001754114,0.000943719,0.0004278549,0.001527844,0.02088892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001862836,"about_ca_system_score_gemma":0.002317187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4531749,"about_ca_topic_score_gemma":0.4417764,"domain_scores_codex":[0.9995925,0.00005813713,0.00002386589,0.00009857124,0.0001638085,0.00006312929],"domain_scores_gemma":[0.9986181,0.0001890369,0.00004956168,0.0001093707,0.000960918,0.00007306115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001440247,0.0001554695,0.02854411,0.0005519922,0.000102507,0.00006482449,0.00008168313,0.07870447,0.0005856525,0.003643003,0.858167,0.02925522],"study_design_scores_gemma":[0.0003569135,0.00004245627,0.08650452,0.0003255875,0.0001088923,0.0001012054,0.0005447157,0.07221001,0.002289479,0.00435171,0.8330297,0.0001347786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00847565,0.0001759106,0.003783275,0.0002033993,0.0001738953,0.0001348942,0.9744085,0.0005389023,0.01210557],"genre_scores_gemma":[0.05965546,0.0003225143,0.01301736,0.0002053227,0.00002776383,0.0004674308,0.910032,0.0008781337,0.01539401],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4531749,"threshold_uncertainty_score":0.9010742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1123100810089834,"score_gpt":0.264024496663943,"score_spread":0.1517144156549595,"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."}}