{"id":"W6977302289","doi":"10.60692/77w6c-ehz37","title":"Soil micronutrients & grassland productivity (NutNet dataset)","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Grassland; Biomass (ecology); Productivity; Nutrient; Soil nutrients; Soil water; Arid; Soil classification","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005316074,0.0009960405,0.0008551132,0.001451363,0.0003363656,0.000754429,0.001465459,0.001327868,0.01211287],"category_scores_gemma":[0.002436971,0.0003516403,0.000804304,0.002934305,0.0002041387,0.0005077815,0.001070956,0.000706098,0.009671271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008490001,"about_ca_system_score_gemma":0.0009843352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04710321,"about_ca_topic_score_gemma":0.06413979,"domain_scores_codex":[0.9996628,0.00005324014,0.00004651803,0.0001063001,0.00008167836,0.0000496485],"domain_scores_gemma":[0.999025,0.0002283402,0.0001704734,0.000169899,0.0002803085,0.0001259532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003589045,0.0001642084,0.04144139,0.002042738,0.0002943512,0.000171578,0.00015693,0.00326844,0.001024003,0.000859763,0.9401843,0.01003339],"study_design_scores_gemma":[0.0008625374,0.0001545164,0.2410308,0.0005860194,0.0002360491,0.0002753928,0.0004224096,0.005762305,0.001107162,0.001782989,0.7476874,0.00009247543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003112254,0.00007692755,0.00005494221,0.00005119795,0.00001061557,0.000009422004,0.996249,0.0001154752,0.000320203],"genre_scores_gemma":[0.004344802,0.00004821372,0.0002996636,0.0000398806,0.000005090225,0.00005543832,0.9949414,0.00001656283,0.0002489606],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04710321,"threshold_uncertainty_score":0.09365803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02452047153595216,"score_gpt":0.1924785282808897,"score_spread":0.1679580567449375,"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."}}