{"id":"W3133869608","doi":"10.1111/2041-210x.13586","title":"A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems","year":2021,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Division of Environmental Biology","keywords":"Computer science; Comparability; Interpretability; Sampling design; Sampling (signal processing); Ecology; Environmental science; Data mining; Data science; Machine learning; Biology; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001075296,0.00005947087,0.0001305196,0.0000399847,0.00006778487,0.000009093912,0.00002943061,0.0001491733,0.00000612087],"category_scores_gemma":[0.0003553205,0.00005409923,0.00001644134,0.0001078655,0.00006089416,0.00006617662,0.000065687,0.00007536988,0.000001422053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000123085,"about_ca_system_score_gemma":0.000006936716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001465664,"about_ca_topic_score_gemma":0.0006719105,"domain_scores_codex":[0.9990551,0.0003453481,0.0001826314,0.0002289734,0.00002628085,0.0001616872],"domain_scores_gemma":[0.9995593,0.0003261395,0.00004003151,0.00004729093,0.000002314313,0.00002491847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004611855,0.00002739652,0.9902932,0.000004572599,0.000001925933,0.000001359966,0.0001157683,0.0004839866,0.0009612321,0.0001145881,0.000002506268,0.007947327],"study_design_scores_gemma":[0.0007531448,0.00005108812,0.8073213,0.000009206695,0.000004507689,0.00001393459,0.0001310075,0.08365029,0.0001914892,0.1075692,0.0002316879,0.00007306295],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9470937,0.0001195717,0.05222631,0.0001068651,0.0002512526,0.0001514321,0.000002092369,0.000006921572,0.00004182471],"genre_scores_gemma":[0.8975795,0.00004004047,0.1022535,0.00002784271,0.00003517978,0.000041007,0.00001439074,0.000002612277,0.000005934953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1829719,"threshold_uncertainty_score":0.2206103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0478349911057924,"score_gpt":0.3415816447999135,"score_spread":0.2937466536941211,"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."}}