{"id":"W6931542765","doi":"10.5281/zenodo.8158516","title":"CMIP forcing datasets update timeline","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Forcing (mathematics); Timeline; Aerosol; Greenhouse gas; Climate model; Climate change; Biomass burning; Radiative forcing; Sulfate aerosol","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":[0.001560588,0.00150691,0.00100498,0.004066046,0.0008248987,0.001927561,0.002048895,0.00155556,0.05552659],"category_scores_gemma":[0.006698588,0.0007384208,0.0009914151,0.009011452,0.0002563794,0.002241561,0.001320033,0.002561551,0.05259196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601033,"about_ca_system_score_gemma":0.00249844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05221095,"about_ca_topic_score_gemma":0.03143436,"domain_scores_codex":[0.999062,0.000107826,0.0001235966,0.0002599497,0.000313813,0.0001328028],"domain_scores_gemma":[0.9965349,0.0004091961,0.0002571782,0.0007510974,0.001824894,0.0002226374],"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":[0.00006167225,0.0000179329,0.001313398,0.0002136665,0.00002952217,0.00002428842,0.00002446256,0.002008011,0.0002264307,0.001454092,0.9831073,0.01151924],"study_design_scores_gemma":[0.0001179032,0.00001022878,0.007153019,0.0001171303,0.0000244706,0.00004982404,0.00003214992,0.001301873,0.0007401171,0.001283509,0.9891305,0.00003925673],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0005855057,0.0001135818,0.001241237,0.0003151245,0.0002621809,0.0000677178,0.9899696,0.001857729,0.005587303],"genre_scores_gemma":[0.002073473,0.0001711909,0.003264332,0.0002378476,0.00005501389,0.0004102746,0.9905373,0.0006620648,0.002588469],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05552659,"threshold_uncertainty_score":0.185755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098601709507143,"score_gpt":0.2495857482938049,"score_spread":0.2185997311987334,"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."}}