{"id":"W6891479270","doi":"10.4224/40002962","title":"NRCan hydrogen codes and standards gap analysis project (NRCan H2 CSGA)","year":2022,"lang":"en","type":"report","venue":"NPARC","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; GDG Environnement","funders":"","keywords":"Software deployment; Pillar; Hydrogen; Theme (computing)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.0110852,0.0008895615,0.0005833121,0.005010805,0.004716502,0.006180298,0.002761212,0.002569674,0.03334879],"category_scores_gemma":[0.02973319,0.0008219305,0.0006292199,0.008571567,0.001360988,0.002324918,0.002364343,0.003331792,0.01205841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05773231,"about_ca_system_score_gemma":0.2988897,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9566824,"about_ca_topic_score_gemma":0.9480686,"domain_scores_codex":[0.9771535,0.0009788811,0.0003086797,0.0006032064,0.01864224,0.002313484],"domain_scores_gemma":[0.9358151,0.003703153,0.00124899,0.001977133,0.0547105,0.002545099],"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":[0.00007370118,0.00006330909,0.001693881,0.000161495,0.000009339738,0.00003915371,0.0002458438,0.0009453,0.0002747429,0.03250294,0.9408061,0.02318426],"study_design_scores_gemma":[0.00004179245,0.00002618447,0.006891761,0.0002129331,0.00001415798,0.00002570546,0.0008194169,0.001348326,0.001588311,0.003430254,0.985548,0.00005320689],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01009935,0.001818498,0.01320793,0.03389883,0.002534151,0.002337405,0.3095405,0.004137273,0.6224261],"genre_scores_gemma":[0.07146972,0.003929594,0.06587756,0.009932039,0.0002821576,0.003223579,0.2899194,0.003120032,0.552246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05773231,"threshold_uncertainty_score":0.4188792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05462778666114203,"score_gpt":0.356252528988064,"score_spread":0.301624742326922,"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."}}