{"id":"W2565716344","doi":"10.1002/2016jg003455","title":"Winter respiratory C losses provide explanatory power for net ecosystem productivity","year":2016,"lang":"en","type":"article","venue":"Journal of Geophysical Research Biogeosciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Russian Science Foundation; Eidgenössische Technische Hochschule Zürich; Ministerstvo Školství, Mládeže a Tělovýchovy; Staatssekretariat für Bildung, Forschung und Innovation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Explanatory power; Productivity; Ecosystem; Environmental science; Power (physics); Natural resource economics; Economics; Ecology; Biology; Physics; Macroeconomics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001712122,0.0005342456,0.0002836742,0.0006679218,0.0001796069,0.0008082045,0.0003074015,0.0003367201,0.00176463],"category_scores_gemma":[0.004575849,0.0002258656,0.0004862549,0.0004673668,0.0002735083,0.0005228305,0.0006001742,0.0004416643,0.000282837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001516531,"about_ca_system_score_gemma":0.0001776094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004632545,"about_ca_topic_score_gemma":0.004388235,"domain_scores_codex":[0.9996601,0.0001424533,0.00002168664,0.00008164077,0.00004090114,0.00005315821],"domain_scores_gemma":[0.9950328,0.003404943,0.0006227209,0.0003272477,0.0002644684,0.0003477531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001164069,0.00002074882,0.9908664,0.00001421707,0.0002068369,0.00004787446,0.00005095982,0.002251778,0.0009425014,0.00007021912,0.0001289878,0.00528302],"study_design_scores_gemma":[0.000002929979,0.00002536461,0.9852417,0.000005882233,0.00003411729,0.00003541417,0.00006473769,0.01402771,0.000198793,0.0002194262,0.0001376179,0.000006235945],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978345,0.0001933469,0.001234543,0.00005161424,0.000007423464,0.00000282288,0.0002883433,0.00002652043,0.0003609384],"genre_scores_gemma":[0.9994887,0.00002724869,0.0001671431,0.000004922611,0.000006481044,0.000001238182,0.0002150794,0.000003746844,0.00008548271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004632545,"threshold_uncertainty_score":0.009211123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03382890686521271,"score_gpt":0.3025206125094806,"score_spread":0.2686917056442679,"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."}}