{"id":"W2106804489","doi":"10.1139/x01-125","title":"A reanalysis of nutrient dynamics in coniferous coarse woody debris","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chronosequence; Coarse woody debris; Nutrient; Environmental science; Flux (metallurgy); Volume (thermodynamics); Biogeochemical cycle; Temperate climate; Nutrient cycle; Atmospheric sciences; Chemistry; Hydrology (agriculture); Ecology; Soil science; Environmental chemistry; Biology; Soil water; Geology; Habitat","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001090913,0.00007119704,0.0002374677,0.0002168814,0.000242246,0.00002555803,0.0003828628,0.00009957473,0.0002592946],"category_scores_gemma":[0.0002922614,0.00003127808,0.0001045666,0.001019838,0.0004190624,0.00008057668,0.00003930634,0.0003391081,0.0000142254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003252554,"about_ca_system_score_gemma":0.0001898343,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05841858,"about_ca_topic_score_gemma":0.9258839,"domain_scores_codex":[0.9987387,0.0001474593,0.0002851749,0.0001111545,0.0002824625,0.0004350643],"domain_scores_gemma":[0.9987789,0.0002545759,0.0001050043,0.00004810824,0.0004967068,0.0003167017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006461517,0.00004654748,0.9933261,0.000003439779,0.00003876921,0.0005269502,0.0001152597,0.00001283842,0.00004507285,0.0009143055,0.001337596,0.003568478],"study_design_scores_gemma":[0.0002451921,0.0006773057,0.9893697,0.00003601391,0.00001366984,0.00006463231,0.002492593,0.00008892928,0.00003624391,0.001798926,0.005104963,0.00007184892],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953212,0.000389148,0.000001108815,0.002810793,0.00005453857,0.000085051,0.00003542647,0.000001350723,0.001301388],"genre_scores_gemma":[0.9993753,0.0002532016,0.00002471462,0.00003318007,0.00005058707,0.000001052006,0.000009104441,4.395004e-7,0.0002523765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8674653,"threshold_uncertainty_score":0.9478515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04853018703266049,"score_gpt":0.270158949255693,"score_spread":0.2216287622230325,"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."}}