{"id":"W4244022466","doi":"10.26434/chemrxiv.12506117","title":"A Kinetic Description of How Interfaces Accelerate Reactions in Micro-Compartments","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Molecular Junctions and Nanostructures","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Basic Energy Sciences; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy","keywords":"Kinetic energy; Chemistry; Computer science; Chemical physics; Materials science; Statistical physics; Physics; Classical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003287313,0.0001931957,0.0002686403,0.0001522078,0.00001383734,0.00005124492,0.0001558347,0.0001595586,0.00004777328],"category_scores_gemma":[0.0000162882,0.0002044399,0.0000783486,0.000163662,0.00002498895,0.0000451068,0.00009976652,0.0003983728,0.000007878903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007025383,"about_ca_system_score_gemma":0.00001649591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003522994,"about_ca_topic_score_gemma":0.00002032283,"domain_scores_codex":[0.9993128,0.00001775844,0.0002349499,0.0002206758,0.00008097551,0.0001328637],"domain_scores_gemma":[0.9996041,0.000007710991,0.00006906873,0.0002464979,0.00002997507,0.00004262535],"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":[0.000009426847,0.00002049386,0.0005834839,0.0004875589,0.0000769714,0.000004967716,0.0001790653,0.008837186,0.9871083,0.00002115459,0.002324005,0.0003473863],"study_design_scores_gemma":[0.0003123132,0.00001822677,0.01196533,0.000284097,0.00004720162,0.000004177576,0.00005421261,0.006126002,0.974717,0.0007608599,0.005434954,0.0002756034],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952529,0.0007695375,0.001672911,0.0001359568,0.0007725012,0.0002270518,0.000007843705,0.0001041206,0.001057166],"genre_scores_gemma":[0.9983578,0.0002200327,0.001157988,0.00001240318,0.00005216029,0.00004215747,0.00005809491,0.00002612043,0.00007328443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01239128,"threshold_uncertainty_score":0.8336816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03466536385176507,"score_gpt":0.2324566456475169,"score_spread":0.1977912817957518,"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."}}