{"id":"W1974840562","doi":"10.1016/j.neucom.2004.10.049","title":"A novel Monte Carlo simulation for molecular interactions and diffusion in postsynaptic spines","year":2004,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Monte Carlo method; Kinetic Monte Carlo; Calmodulin; Diffusion; Postsynaptic potential; Statistical physics; Computer science; Molecular dynamics; Monte Carlo molecular modeling; Dendrite (mathematics); Biological system; Chemistry; Physics; Computational chemistry; Markov chain Monte Carlo; Mathematics; Biology; Thermodynamics; Nuclear magnetic resonance","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":[0.0008357163,0.0005077946,0.001302046,0.0006947493,0.001454642,0.001047032,0.002694686,0.00309773,0.004366632],"category_scores_gemma":[0.002227143,0.0007107944,0.0009405884,0.0008252603,0.001175743,0.0009780434,0.001092265,0.001423763,0.0004689141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001423235,"about_ca_system_score_gemma":0.002297627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01648652,"about_ca_topic_score_gemma":0.01771033,"domain_scores_codex":[0.9997216,0.00007431183,0.00001197771,0.00003090563,0.0001131395,0.00004800088],"domain_scores_gemma":[0.9987941,0.0006669137,0.00006052216,0.0001071829,0.0001861668,0.000185077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008318338,0.0001005917,0.0006173814,0.00004285055,0.00005372198,0.000107165,0.00003732419,0.9710806,0.001804785,0.0212776,0.0007309398,0.004063797],"study_design_scores_gemma":[0.0000209929,0.00000535835,0.00003677412,0.000001834574,0.000003823562,0.000006874355,0.000002327982,0.9983419,0.0001158771,0.001288337,0.0001722915,0.000003590667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2009437,0.0006338787,0.7755092,0.001162003,0.00039318,0.0002912879,0.0004509063,0.001362224,0.01925365],"genre_scores_gemma":[0.6913732,0.0003632949,0.2986797,0.0005279446,0.0001820476,0.0007800708,0.0003901215,0.0005537534,0.00714991],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01648652,"threshold_uncertainty_score":0.03278112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008286046233771256,"score_gpt":0.2682761027373768,"score_spread":0.2599900565036056,"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."}}