{"id":"W4238353704","doi":"10.1016/s1464-2859(18)30353-5","title":"Hydrogenics fuel cells for heavy-duty trucks in California project","year":2018,"lang":"en","type":"article","venue":"Fuel Cells Bulletin","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Heavy duty; Engineering; Transport engineering; Fuel cells; Aeronautics; Automotive engineering; Waste management; Environmental science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004372314,0.000410308,0.0004858238,0.0001839003,0.00007867829,0.00008725069,0.000324339,0.0004302815,0.001133681],"category_scores_gemma":[0.00002062854,0.0003823502,0.0001715213,0.0002416509,0.0001056426,0.00003726665,0.00007350888,0.000273341,0.002642066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001127804,"about_ca_system_score_gemma":0.00004942368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001174387,"about_ca_topic_score_gemma":0.00005022484,"domain_scores_codex":[0.9978412,0.00005793845,0.0006407373,0.0004577268,0.0002042811,0.00079816],"domain_scores_gemma":[0.9991794,0.0001037519,0.00008267699,0.0004250671,0.00007456746,0.000134505],"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":[0.0008217347,0.0005728867,0.00006325319,0.01405033,0.0003142525,0.0001370979,0.003235976,0.01702059,0.4140857,0.0001236858,0.5483112,0.001263202],"study_design_scores_gemma":[0.001126857,0.0001612665,0.000004911373,0.00007761943,0.00003311481,0.000007962971,0.00005399161,0.006815864,0.1928108,0.0003706226,0.7980513,0.000485735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6302378,0.03126348,0.002553435,0.001538053,0.02736234,0.01102051,0.003077457,0.003651348,0.2892956],"genre_scores_gemma":[0.9828146,0.00354462,0.008014912,0.0003779405,0.001291817,0.0002404456,0.00009897982,0.0002954966,0.003321236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3525768,"threshold_uncertainty_score":0.9998628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009406869485942071,"score_gpt":0.2111162897155408,"score_spread":0.2017094202295987,"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."}}