{"id":"W4394293868","doi":"10.6084/m9.figshare.3514139","title":"Supplement 1. Data set containing soil climate and carbon flux data for years 2001–2005 at Northern Old Black Spruce Forest, Manitoba, Canada.","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Carbon flux; Black spruce; Flux (metallurgy); Forestry; Environmental science; Carbon black; Data set; Carbon fibers; Physical geography; Climatology; Geography; Atmospheric sciences; Taiga; Ecology; Geology; Mathematics; Statistics; Chemistry; Ecosystem","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008043745,0.001060491,0.00128087,0.003552948,0.001760065,0.002270917,0.002569808,0.0005943876,0.4096136],"category_scores_gemma":[0.005328762,0.0007697468,0.0006318017,0.01096731,0.000326498,0.001007598,0.0008858044,0.0008816448,0.08948793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01058358,"about_ca_system_score_gemma":0.02811805,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9122266,"about_ca_topic_score_gemma":0.9427168,"domain_scores_codex":[0.9994687,0.00002823002,0.00006376839,0.00008593489,0.0002387367,0.0001145681],"domain_scores_gemma":[0.9857249,0.001353344,0.0008075808,0.000679421,0.01073494,0.0006997298],"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":[0.00004683433,0.00002395696,0.002736958,0.000361706,0.00001829564,0.00002554277,0.00004981634,0.0002213368,0.00006267693,0.0002732688,0.9924942,0.003685362],"study_design_scores_gemma":[0.0003918138,0.00002867373,0.08573899,0.0004694195,0.00004699862,0.0000942044,0.0004648495,0.0006283253,0.0004642189,0.0008509104,0.9107629,0.00005875012],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001535474,0.00001165264,0.00003111758,0.00003592586,0.00001994951,0.00001949091,0.9984825,0.00008804704,0.001157826],"genre_scores_gemma":[0.002404886,0.00005969164,0.0004652096,0.00006247724,0.00001655182,0.0001413918,0.9896795,0.0001428024,0.007027615],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4096136,"threshold_uncertainty_score":0.8421145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1282099002617077,"score_gpt":0.2758336538815224,"score_spread":0.1476237536198147,"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."}}