{"id":"W2000652200","doi":"10.1890/09-0693.1","title":"Looking deeper into the soil: biophysical controls and seasonal lags of soil CO<sub>2</sub>production and efflux","year":2010,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Environmental science; Primary production; Ecosystem; Vegetation (pathology); Seasonality; Atmospheric sciences; Phenology; Flux (metallurgy); Moderate-resolution imaging spectroradiometer; Terrestrial ecosystem; Ecology; Chemistry; Biology; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000152647,0.0001072467,0.0001231686,0.000002732602,0.0002751768,0.00001655413,0.000122816,0.00009211652,0.0001333214],"category_scores_gemma":[0.00003237761,0.00007277316,0.00003249349,0.00008933305,0.001145994,0.00007118669,0.0001635794,0.0002038834,0.00005732507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003441949,"about_ca_system_score_gemma":0.000003911662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000310041,"about_ca_topic_score_gemma":0.000161609,"domain_scores_codex":[0.9992408,0.00002555118,0.0001434774,0.0002997916,0.0001371029,0.0001532384],"domain_scores_gemma":[0.9995439,0.0001147206,0.00007000654,0.0001821081,0.000003985518,0.00008529347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001561093,0.0003526987,0.1776351,0.00000693295,0.00001714835,4.596946e-7,0.0002305038,0.003085095,0.7816608,0.003476227,0.0001614368,0.03335795],"study_design_scores_gemma":[0.0002108347,0.00007943466,0.9731084,0.000001967349,0.00003417073,0.00001325114,0.0001281619,0.007543731,0.009374578,0.006837497,0.002505595,0.0001623503],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949516,0.0000142289,0.00319496,0.00102939,0.00002631538,0.0003255696,0.000002365185,0.00002293099,0.0004326484],"genre_scores_gemma":[0.9970248,0.00006877199,0.002374103,0.0002065946,0.00006743189,0.0001849842,0.000004268851,0.000007330989,0.00006172231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7954733,"threshold_uncertainty_score":0.4222462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003337279902026747,"score_gpt":0.1963863493207584,"score_spread":0.1930490694187316,"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."}}