{"id":"W2236234533","doi":"10.1139/cjfr-2015-0512","title":"Modelling moss-derived carbon in upland black spruce forests","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Canadian Forest Service; Natural Resources Canada","funders":"Natural Resources Canada; McGill University","keywords":"Sphagnum; Moss; Black spruce; Environmental science; Taiga; Canopy; Peat; Boreal; Forestry; Bog; Atmospheric sciences; Ecology; Botany; Biology; Geography; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0002987008,0.0006676132,0.0002975261,0.0003544514,0.0004275497,0.0006568275,0.0008346504,0.0005391429,0.0003575895],"category_scores_gemma":[0.0007001611,0.000340324,0.0004102746,0.0003142626,0.0004125495,0.000459441,0.0003500249,0.0002743236,0.00006654871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002267804,"about_ca_system_score_gemma":0.001786977,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3558296,"about_ca_topic_score_gemma":0.3716913,"domain_scores_codex":[0.999915,0.00001375503,0.000003605498,0.00002678519,0.00001407287,0.00002675404],"domain_scores_gemma":[0.9997409,0.000108225,0.00004967664,0.00001080463,0.00003874411,0.00005165509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005825239,0.00004371349,0.0405619,0.00002539743,0.00005394566,0.0001175877,0.00005324522,0.9513903,0.004555756,0.000394587,0.00007523129,0.002670133],"study_design_scores_gemma":[0.00001535637,0.00002231327,0.02383615,0.000005971622,0.00001548789,0.00001791011,0.00004545404,0.9746948,0.0007783029,0.0003713403,0.000184526,0.00001237807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970838,0.0001068935,0.001903912,0.00002701991,0.000003576579,0.000008232831,0.0001371804,0.00003575906,0.0006935371],"genre_scores_gemma":[0.9987196,0.00005191774,0.000901697,0.000006580098,0.000002090843,0.000005214945,0.00008460677,0.000006698805,0.0002216464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6441704,"threshold_uncertainty_score":0.7075168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04085109177795385,"score_gpt":0.2813965855592856,"score_spread":0.2405454937813317,"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."}}