{"id":"W2620172483","doi":"10.1038/s41598-017-02580-9","title":"Decadal Variations in Eastern Canada’s Taiga Wood Biomass Production Forced by Ocean-Atmosphere Interactions","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Center for Northern Studies; Institut National de la Recherche Scientifique; Université du Québec à Rimouski; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; ArcticNet; Manitoba Hydro","keywords":"Taiga; Environmental science; Black spruce; Biomass (ecology); Atmosphere (unit); Boreal; Climate change; Productivity; Ecosystem; Climatology; Oceanography; Atmospheric sciences; Ecology; Geography; Geology; Biology; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007401269,0.000145638,0.0001543248,0.00005812768,0.001176586,0.000753205,0.0002790734,0.00004648997,0.0006626921],"category_scores_gemma":[0.0006831632,0.0001331457,0.00004339037,0.0001899799,0.0001628303,0.0007579667,0.00003281462,0.0001309197,0.00004673706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005214601,"about_ca_system_score_gemma":0.0007798146,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4892616,"about_ca_topic_score_gemma":0.9622588,"domain_scores_codex":[0.997953,0.00006092637,0.0004263315,0.0006812781,0.000491427,0.0003870211],"domain_scores_gemma":[0.998271,0.00005987795,0.0004342387,0.000982209,0.00009901179,0.0001535952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002013306,0.0000310657,0.9513212,0.00001510775,0.00001476711,0.0002219152,0.0001921318,0.0007284545,0.001668262,0.000003463322,0.04150847,0.004275055],"study_design_scores_gemma":[0.000162313,0.00002480141,0.8878113,0.00007684072,0.00002061819,0.0003419822,0.0002684151,0.004241674,0.004884128,0.0005872777,0.1012492,0.0003314655],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807044,0.00007914841,0.00003190067,0.001209439,0.01395878,0.000249116,0.00004882367,0.00004828216,0.003670104],"genre_scores_gemma":[0.9838283,0.000002059582,0.0001901676,0.00001174905,0.00007420011,0.000002198537,0.0001872194,0.000005747239,0.01569833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4729972,"threshold_uncertainty_score":0.9049465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01616719593885351,"score_gpt":0.243813896673718,"score_spread":0.2276467007348645,"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."}}