{"id":"W6889730834","doi":"10.25925/v0va-cy20","title":"CarbonTracker Near-Real Time, CT-NRT.v2022-1","year":2022,"lang":"en","type":"dataset","venue":"Global Monitoring Laboratory","topic":"","field":"","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Carbon dioxide in Earth's atmosphere; Atmosphere (unit); Carbon dioxide; Track (disk drive); Atmospheric model; Atmospheric composition","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":[0.001138766,0.002508616,0.001160435,0.001759282,0.00101768,0.002026141,0.003496466,0.002187587,0.02433013],"category_scores_gemma":[0.003756101,0.0007004054,0.0008820267,0.003832236,0.0005467651,0.002076445,0.00160331,0.001737364,0.04254686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001617829,"about_ca_system_score_gemma":0.002752451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09259721,"about_ca_topic_score_gemma":0.1505931,"domain_scores_codex":[0.9989592,0.0001526546,0.00007889208,0.0003385244,0.0003088358,0.000162011],"domain_scores_gemma":[0.9983942,0.000176002,0.0001405118,0.0004775033,0.0005978904,0.0002137731],"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":[0.0001096742,0.00002874096,0.001103147,0.0002193859,0.00002630355,0.00002212169,0.00001694913,0.00119242,0.0002512694,0.0003542882,0.9950498,0.00162579],"study_design_scores_gemma":[0.0006852867,0.00005618597,0.0113605,0.000230122,0.00004376394,0.0000979174,0.0001241248,0.009043261,0.001669982,0.002335178,0.9742347,0.000118936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003343099,0.00003345422,0.0001175131,0.00007544814,0.00003619917,0.00001491623,0.9977392,0.0009613779,0.0006875483],"genre_scores_gemma":[0.0005651922,0.00001582402,0.0004139967,0.00003775071,0.00000683075,0.00003590602,0.9984766,0.00009318582,0.0003547838],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09259721,"threshold_uncertainty_score":0.1841165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008777081243016129,"score_gpt":0.2689947942809282,"score_spread":0.260217713037912,"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."}}