{"id":"W4248397457","doi":"10.5194/bg-2021-136-rc1","title":"Comment on bg-2021-136","year":2021,"lang":"en","type":"peer-review","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Span (engineering); Permafrost; Greenhouse gas; Environmental science; Atmosphere (unit); Meteorology; Climate change; Benchmark (surveying); Lag; Atmospheric sciences; Series (stratigraphy); Time lag; Physics; Computer science; Geology; Engineering; Geodesy; Structural engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002125417,0.001068734,0.001290395,0.001525549,0.00225852,0.006499501,0.002420587,0.02091925,0.1484794],"category_scores_gemma":[0.01374507,0.000589493,0.00162529,0.002770213,0.00139945,0.002427498,0.001780135,0.01200045,0.1190302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005079505,"about_ca_system_score_gemma":0.005900064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06044908,"about_ca_topic_score_gemma":0.04717602,"domain_scores_codex":[0.9979729,0.0001348689,0.0001188522,0.0002163049,0.00123137,0.0003258009],"domain_scores_gemma":[0.9956363,0.001004754,0.0002292488,0.0004048822,0.002306788,0.0004178827],"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":[0.00005459478,0.00001176094,0.00006855743,0.00004357797,0.000002644638,0.00003714086,0.000006843518,0.00004692493,0.00008063954,0.0006345115,0.99669,0.002322693],"study_design_scores_gemma":[0.0000508434,0.00001366086,0.00149094,0.00008496229,0.000006270855,0.00001873273,0.00003663702,0.0001626611,0.0002733164,0.001418465,0.99642,0.00002354027],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.00111593,0.001763094,0.001025468,0.3737369,0.3446947,0.0005602672,0.04289883,0.004828336,0.2293765],"genre_scores_gemma":[0.008794945,0.00138037,0.001461234,0.4779623,0.05207512,0.0006948731,0.01423735,0.002055983,0.4413379],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.1484794,"threshold_uncertainty_score":0.496713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01398267502119331,"score_gpt":0.2436842554996525,"score_spread":0.2297015804784592,"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."}}