{"id":"W3160528426","doi":"10.2172/1782589","title":"Next Generation Hydrogen Station Composite Data Products: Retail Stations; Summer 2020 (Q2 FY2020)","year":2021,"lang":"en","type":"report","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Renewable Energy Laboratory","keywords":"Software deployment; Reliability (semiconductor); Composite number; Quarter (Canadian coin); Hydrogen production; Environmental science; Data quality; Component (thermodynamics); Hydrogen; Environmental economics; Computer science; Business; Service (business); Marketing; Chemistry; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002200587,0.001414707,0.0004452328,0.001261321,0.0005462739,0.002637848,0.0009997422,0.0009474701,0.08953639],"category_scores_gemma":[0.004012608,0.0005153121,0.00033818,0.002022312,0.0001779797,0.001345811,0.0007599924,0.001017907,0.07525569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002487302,"about_ca_system_score_gemma":0.007487672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07951023,"about_ca_topic_score_gemma":0.07047956,"domain_scores_codex":[0.9988461,0.00006046906,0.00003389514,0.00007708264,0.0008936914,0.00008878354],"domain_scores_gemma":[0.99669,0.0002046333,0.0002095944,0.0001281822,0.002465265,0.0003023047],"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":[0.0001169041,0.00005295673,0.0008045431,0.00007506575,0.000005014724,0.000008104063,0.000009186226,0.0004921402,0.000289567,0.0005000985,0.9824458,0.01520068],"study_design_scores_gemma":[0.00008004442,0.0000939759,0.00733216,0.0000590058,0.000009083529,0.00001187544,0.00005078983,0.00138215,0.001548455,0.0004504197,0.9889656,0.00001638932],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003722857,0.0004187727,0.003661989,0.001880659,0.001455809,0.0006623369,0.8406942,0.002018752,0.1454846],"genre_scores_gemma":[0.01123155,0.0008443907,0.009556123,0.0005257,0.0002304822,0.0007108998,0.7990283,0.00080616,0.1770664],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08953639,"threshold_uncertainty_score":0.2995291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.670532853712078,"score_gpt":0.4818620424417646,"score_spread":0.1886708112703135,"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."}}