{"id":"W4389541338","doi":"10.17118/11143/20828","title":"Morphology and agglomeration of copper chloride particles in thethermochemical Cu-Cl cycle of hydrogen production","year":2023,"lang":"en","type":"article","venue":"","topic":"Chemical Looping and Thermochemical Processes","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Nuclear Laboratories","keywords":"Economies of agglomeration; Copper; Hydrogen production; Hydrogen; Metallurgy; Hydrogen chloride; Chloride; Materials science; Morphology (biology); Production (economics); Copper chloride; Chemical engineering; Chemistry; Inorganic chemistry; Engineering; Organic chemistry; Economics; Geology","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":[0.0001688283,0.0001557615,0.0001252671,0.0004729401,0.0002269475,0.0002375281,0.0002035393,0.0002761874,0.0005966565],"category_scores_gemma":[0.0002057271,0.0001222683,0.000156301,0.0002034452,0.0002584969,0.0001379124,0.00009632132,0.0001425087,0.0001502109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005040908,"about_ca_system_score_gemma":0.0001176652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003706696,"about_ca_topic_score_gemma":0.003836104,"domain_scores_codex":[0.9998627,0.00001154041,0.000007179722,0.00003068054,0.00006685199,0.0000211023],"domain_scores_gemma":[0.9998428,0.00003797385,0.00003164678,0.00001309948,0.00006119211,0.00001327434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002945173,0.00002404187,0.002951923,0.00005530492,0.000009464805,0.0002713301,0.0001263922,0.00108973,0.9915199,0.0001036162,0.0001321788,0.003421591],"study_design_scores_gemma":[0.000009246893,0.0001472055,0.02707166,0.000003663443,0.000009204432,0.0001531048,0.00005872631,0.00620948,0.9657638,0.00002939284,0.0005335313,0.00001109284],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979057,0.000178227,0.000757124,0.00001143103,0.000004008743,0.00001007683,0.0001273634,0.00004294137,0.0009631045],"genre_scores_gemma":[0.9988704,0.00003233296,0.0003889159,0.000005720992,0.000001215821,0.000007145016,0.00009705657,0.00001151566,0.0005856695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003706696,"threshold_uncertainty_score":0.007370234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188100154508896,"score_gpt":0.2264633542462855,"score_spread":0.2145823527011965,"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."}}