{"id":"W2021530899","doi":"10.14214/sf.128","title":"Carbon stocks in managed conifer forests in northern Ontario, Canada","year":2010,"lang":"en","type":"article","venue":"Silva Fennica","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; University of Guelph","funders":"","keywords":"Understory; Forestry; Black spruce; Forest floor; Environmental science; Coarse woody debris; Taiga; Primary production; Productivity; Carbon fibers; Biomass (ecology); Agronomy; Agroforestry; Ecosystem; Ecology; Soil water; Biology; Geography; Habitat; Canopy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0001667679,0.0001686042,0.0001720383,0.0008353771,0.001537585,0.0006548754,0.0003769786,0.0001470493,0.001062194],"category_scores_gemma":[0.000454912,0.0001750949,0.000113169,0.001847723,0.0004352236,0.000219285,0.0002544076,0.000111891,0.0001626491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01602734,"about_ca_system_score_gemma":0.007584315,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9881353,"about_ca_topic_score_gemma":0.9978768,"domain_scores_codex":[0.9998397,0.000007142556,0.00001081653,0.00002951444,0.00005696675,0.00005578409],"domain_scores_gemma":[0.9994895,0.00002790413,0.0001011059,0.00001554575,0.0002424588,0.0001233579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001009516,0.00002445888,0.9874676,0.00004015986,0.00004180064,0.000197156,0.001700456,0.0002630804,0.002055371,0.0001165313,0.0009009177,0.00709157],"study_design_scores_gemma":[0.000003512064,0.000005361477,0.9985195,0.000006061355,0.000005148755,0.0000252776,0.0005241283,0.0001062866,0.00005015233,0.000009024926,0.0007430512,0.000002564956],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973001,0.0001991924,0.00002282795,0.00003040647,0.000001636423,0.000008312128,0.001025418,0.000004463526,0.001407577],"genre_scores_gemma":[0.9972167,0.0002150389,0.000111303,0.0000215699,0.000001510181,0.0000109584,0.0009502969,0.000002477097,0.001470106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01602734,"threshold_uncertainty_score":0.1162871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008666449676582378,"score_gpt":0.1727337088692067,"score_spread":0.1640672591926243,"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."}}