{"id":"W2005155060","doi":"10.1063/1.4938783","title":"Clusters: From trimers to nanoparticles","year":2015,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Nanocluster Synthesis and Applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Nanoparticle; Computer science; Materials science; Nanotechnology","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":[0.00009182956,0.0001966289,0.0002836913,0.0004057896,0.0005705136,0.001110958,0.0006717127,0.0007247971,0.009484503],"category_scores_gemma":[0.0003506398,0.0003205005,0.0001740496,0.0002114968,0.0007014073,0.001275179,0.0007090981,0.0007803411,0.00242591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005794429,"about_ca_system_score_gemma":0.0002019643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007007312,"about_ca_topic_score_gemma":0.001003686,"domain_scores_codex":[0.9998995,0.000008867664,0.000003880722,0.00004285929,0.00002538864,0.0000194742],"domain_scores_gemma":[0.9999124,0.00001685298,0.00001219587,0.00002174908,0.00001521251,0.00002163885],"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":[0.0004336113,0.0001530037,0.001072892,0.001074633,0.00008997355,0.0005369293,0.001069837,0.00596624,0.6700464,0.200995,0.04141745,0.07714411],"study_design_scores_gemma":[0.00006844354,0.0003978182,0.002373363,0.0001451784,0.00006978056,0.0009404111,0.000637093,0.02543757,0.6413466,0.20438,0.124102,0.0001016595],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7838304,0.02924196,0.06308133,0.008261893,0.002308757,0.0001687953,0.001094453,0.003884307,0.1081281],"genre_scores_gemma":[0.9322554,0.003911167,0.01058397,0.001014151,0.0002732316,0.00009382856,0.0006196401,0.0004952296,0.05075331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009484503,"threshold_uncertainty_score":0.0317288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0568902081205326,"score_gpt":0.2752513521742267,"score_spread":0.2183611440536941,"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."}}