{"id":"W6944108996","doi":"10.18140/flx/1669613","title":"FLUXNET-CH4 CA-SCB Scotty Creek Bog","year":2020,"lang":"en","type":"dataset","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"","field":"","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hydrology (agriculture); Flux (metallurgy); Bog; Scale (ratio); Carbon flux","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006535166,0.001795215,0.00121882,0.002616109,0.0005696529,0.001713113,0.002462953,0.001463099,0.06323948],"category_scores_gemma":[0.002391574,0.0006923322,0.001085694,0.005455005,0.0003361699,0.001379563,0.001148356,0.001468319,0.06246085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426098,"about_ca_system_score_gemma":0.00228828,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09216346,"about_ca_topic_score_gemma":0.1173306,"domain_scores_codex":[0.9994727,0.00006659217,0.00004318618,0.000162152,0.0001536492,0.0001017176],"domain_scores_gemma":[0.9989524,0.0001530521,0.0001199532,0.0002263984,0.0004004228,0.0001476882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004415119,0.000008782125,0.0006268519,0.0002645868,0.00002707537,0.000009751697,0.00001094706,0.0003266587,0.00007700259,0.0005153148,0.9969944,0.001094526],"study_design_scores_gemma":[0.0002010652,0.00001259674,0.006660543,0.0002087277,0.00002759722,0.000030649,0.00005873773,0.000690873,0.0002945745,0.001227868,0.9905536,0.00003319372],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007456475,0.00001998323,0.00002563882,0.00002459053,0.00001177533,0.00000248063,0.9991636,0.0001795129,0.0004979151],"genre_scores_gemma":[0.0002970559,0.00002115022,0.0001136358,0.00001898196,0.00000376561,0.00001460893,0.9990282,0.00006799369,0.0004346281],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9367605,"threshold_uncertainty_score":0.2115571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0157367747590495,"score_gpt":0.2426145340131365,"score_spread":0.226877759254087,"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."}}