{"id":"W2015168238","doi":"10.1039/c4cp05383d","title":"Variabilities and uncertainties in characterising water transport kinetics in glassy and ultraviscous aerosol","year":2015,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Glass properties and applications","field":"Materials Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Engineering and Physical Sciences Research Council; Natural Environment Research Council; Sight Research UK","keywords":"Aerosol; Kinetics; Environmental science; Meteorology; Atmospheric sciences; Thermodynamics; Statistical physics; Geology; Physics; Classical mechanics","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":[0.003530135,0.0004553977,0.0004642106,0.001355431,0.0004301891,0.0006888831,0.0006508702,0.0005705487,0.0003596977],"category_scores_gemma":[0.008933141,0.0004232004,0.0005659409,0.0008634885,0.0008763415,0.001247851,0.000700612,0.0005457014,0.0001123005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007776498,"about_ca_system_score_gemma":0.0002903208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002466634,"about_ca_topic_score_gemma":0.002094545,"domain_scores_codex":[0.9981931,0.0003131551,0.0001383126,0.0004135067,0.0008302673,0.000111788],"domain_scores_gemma":[0.9923549,0.004812235,0.00108441,0.001063207,0.0006037133,0.00008155292],"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":[0.0006654369,0.0001246635,0.08426008,0.0003901894,0.0003773645,0.0004005571,0.0006588991,0.07202511,0.8088249,0.00137311,0.0001343511,0.03076532],"study_design_scores_gemma":[0.00001516598,0.0004708551,0.1076983,0.00004210825,0.000142478,0.0005554395,0.000297261,0.1890517,0.6981891,0.002248787,0.001175555,0.0001131791],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678201,0.0004447486,0.030909,0.00003009683,0.00001133349,0.00002854207,0.0002133406,0.0001402605,0.0004024853],"genre_scores_gemma":[0.9935883,0.0002258386,0.005724538,0.00001210303,0.000006908658,0.00002900377,0.0002275146,0.00004576675,0.0001400524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003530135,"threshold_uncertainty_score":0.01866931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719223330071913,"score_gpt":0.2285000184230244,"score_spread":0.2113077851223053,"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."}}