{"id":"W6901928046","doi":"10.6073/pasta/b7b5ce28877a8335dcda39fa196e2e5a","title":"Soil percent carbon and nitrogen:Dimensions of Biodiversity - Genetic, Phylogenetic, Functional, and Remotely Sensed Diversity","year":2018,"lang":"en","type":"dataset","venue":"Environmental Data Initiative","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biodiversity; Biomass (ecology); Ecosystem; Trophic level; Sampling (signal processing); Phylogenetic diversity; Species diversity; Ecosystem diversity; Canopy; Diversity index","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":[0.0006679989,0.0002406752,0.0001556169,0.000752128,0.0002108153,0.000776741,0.0002290326,0.0002755075,0.001210981],"category_scores_gemma":[0.00108199,0.0001405704,0.0001333643,0.0008996156,0.0004215235,0.0009366888,0.0004977982,0.0002550276,0.0001331286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003458732,"about_ca_system_score_gemma":0.0001903235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002756336,"about_ca_topic_score_gemma":0.008257349,"domain_scores_codex":[0.9996639,0.0001190048,0.00001982131,0.00007423134,0.0001011712,0.00002185303],"domain_scores_gemma":[0.9990701,0.0002636905,0.000333874,0.00008120661,0.0001136654,0.000137577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004293282,0.000116105,0.944873,0.00009625058,0.0002709775,0.0000616187,0.0002235191,0.002610683,0.02277792,0.001299565,0.0002873038,0.0269538],"study_design_scores_gemma":[0.000006444996,0.00007830712,0.9942556,0.000008704385,0.00001791404,0.00008715669,0.0002065338,0.00201849,0.001805755,0.000989135,0.0005159396,0.000009908425],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.992995,0.000510678,0.002695165,0.0001119066,0.000007379293,0.00001722055,0.001045053,0.00001604369,0.002601649],"genre_scores_gemma":[0.9977586,0.0001049872,0.001372515,0.00002399482,0.00000648994,0.00002152041,0.0003478282,0.00000291688,0.0003609792],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.002756336,"threshold_uncertainty_score":0.005480647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05272328970432545,"score_gpt":0.2305382504211321,"score_spread":0.1778149607168066,"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."}}