{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001064401,0.001584198,0.001461831,0.0002574354,0.0009761466,0.000645224,0.002524725,0.0005753926,0.01284735],"category_scores_gemma":[0.0003855955,0.001911017,0.0003792818,0.003211038,0.0004092491,0.0004552381,0.001578626,0.002302607,0.01841665],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005859181,"about_ca_system_score_gemma":0.002895798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006182485,"about_ca_topic_score_gemma":0.00009681535,"domain_scores_codex":[0.9911602,0.000879508,0.001106495,0.001961062,0.002940181,0.001952575],"domain_scores_gemma":[0.9943932,0.0001515942,0.0009062815,0.003145091,0.0005600401,0.0008438513],"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":[0.0001220304,0.0003207406,0.003453269,0.0001385378,0.0004628734,0.002369831,0.00001480626,0.0001356427,0.000226513,0.000009559416,0.9926066,0.0001395584],"study_design_scores_gemma":[0.0008526961,0.0001267696,0.00345298,0.000111728,0.0005538895,0.00004416324,0.00008535214,0.000004912251,0.00007306114,0.0000154128,0.9930199,0.00165907],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01591287,0.002775167,4.116069e-8,0.00002035055,0.01260196,0.0007220679,0.9657637,0.001131319,0.001072488],"genre_scores_gemma":[0.0002701312,0.0003036612,0.0001500934,0.00007164568,0.003989387,0.0003657918,0.9942778,0.0003299258,0.0002416064],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02851402,"threshold_uncertainty_score":0.9999991,"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."}}