{"id":"W4404073012","doi":"10.3390/data9110129","title":"Data Hub for Life Cycle Assessment of Climate Change Solutions—Hydrogen Case Study","year":2024,"lang":"en","type":"article","venue":"Data","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Innovation Council; National Research Council Canada","funders":"","keywords":"Climate change; Life-cycle assessment; Environmental science; Climatology; Computer science; Economics; Oceanography; Geology","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":[0.003852134,0.0005142348,0.0003662032,0.001505534,0.0007504034,0.001736661,0.001260747,0.001133977,0.005564605],"category_scores_gemma":[0.003997864,0.0002458451,0.0006664117,0.002105106,0.000549701,0.002456623,0.001816461,0.0008138574,0.001238308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001702067,"about_ca_system_score_gemma":0.002445527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00788461,"about_ca_topic_score_gemma":0.005315555,"domain_scores_codex":[0.9987938,0.0003900007,0.0001045162,0.0001942836,0.0003760507,0.0001413132],"domain_scores_gemma":[0.9969304,0.0009442793,0.0001364561,0.0008900716,0.0007360103,0.0003628189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002385236,0.001487717,0.04463097,0.001751087,0.000246643,0.006843133,0.003273265,0.3363999,0.03653941,0.130021,0.07161285,0.3648087],"study_design_scores_gemma":[0.0003685014,0.0006047559,0.01840684,0.0003055016,0.0001075185,0.0007124335,0.003102941,0.6468836,0.06907508,0.03632241,0.2239071,0.0002032984],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.4173719,0.0005869671,0.4890094,0.003415346,0.0003567109,0.003536841,0.02932857,0.01390599,0.0424882],"genre_scores_gemma":[0.691199,0.0004404569,0.2851645,0.0001759916,0.00005282379,0.00104271,0.01473221,0.0004776979,0.006714536],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.00788461,"threshold_uncertainty_score":0.02037227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1567883160249525,"score_gpt":0.4059222087149405,"score_spread":0.249133892689988,"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."}}