{"id":"W2616142637","doi":"","title":"Materials in extreme environments for energy, accelerators and space applications at ELI-NP.","year":2016,"lang":"en","type":"article","venue":"Research Portal (Queen's University Belfast)","topic":"Spacecraft and Cryogenic Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Nuclear Physics; Queen's University; Queen's University Belfast; European Commission","keywords":"Space (punctuation); Nuclear physics; Physics; Energy (signal processing); Environmental science; Astrobiology; Nuclear engineering; Aerospace engineering; Engineering physics; Engineering; Computer science; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001386923,0.0004466427,0.0003489991,0.0008503845,0.002797208,0.00164684,0.001189003,0.001126535,0.03730948],"category_scores_gemma":[0.0007362689,0.0002803539,0.0002924549,0.0007026929,0.000632251,0.0012074,0.003102846,0.00112618,0.01169314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002782049,"about_ca_system_score_gemma":0.003876718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004105651,"about_ca_topic_score_gemma":0.01137086,"domain_scores_codex":[0.9993812,0.00005696724,0.00001360964,0.00008108206,0.0003364467,0.0001307374],"domain_scores_gemma":[0.9993892,0.00008640342,0.0000650556,0.00007867579,0.0001596004,0.0002210538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001046037,0.0005982391,0.02186874,0.001727163,0.00003699245,0.003237487,0.003438946,0.004516893,0.2671967,0.03727638,0.2356846,0.4233718],"study_design_scores_gemma":[0.00009164806,0.0005758539,0.0208713,0.000182027,0.00001823674,0.002092595,0.001410521,0.001460116,0.06080708,0.005727173,0.9067143,0.00004903317],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3203226,0.01930209,0.03696029,0.02497254,0.00357165,0.00127307,0.01517798,0.005402441,0.5730173],"genre_scores_gemma":[0.5239415,0.004826223,0.05912821,0.002023143,0.0004611549,0.0006458701,0.009438032,0.001062616,0.3984733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03730948,"threshold_uncertainty_score":0.1248127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02896175326368815,"score_gpt":0.2384698236235367,"score_spread":0.2095080703598486,"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."}}