{"id":"W4296177994","doi":"10.1016/j.bpj.2022.09.012","title":"LAWS: Local alignment for water sites—Tracking ordered water in simulations","year":2022,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Spectroscopy and Quantum Chemical Studies","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Ontario Research Foundation; Canada Foundation for Innovation; Compute Canada; University of Toronto","keywords":"Protein crystallization; Molecular dynamics; Crystal (programming language); Tracking (education); Stability (learning theory); Crystal structure; Algorithm; Computer science; Set (abstract data type); Basis (linear algebra); Biological system; Crystallography; Statistical physics; Chemistry; Physics; Mathematics; Crystallization; Computational chemistry; Geometry; Thermodynamics; Machine learning; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008410648,0.0003767916,0.0004713148,0.0007983228,0.0008626003,0.001331469,0.002017155,0.001497056,0.005961753],"category_scores_gemma":[0.007037517,0.0004294332,0.0004995493,0.0005460107,0.001585491,0.003296833,0.001065705,0.00162524,0.0005380004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008919589,"about_ca_system_score_gemma":0.001057892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008300621,"about_ca_topic_score_gemma":0.004943907,"domain_scores_codex":[0.9997311,0.00007255074,0.00001564864,0.00006238532,0.00008496224,0.0000333438],"domain_scores_gemma":[0.998287,0.0007411414,0.0001693008,0.0003863166,0.0001847097,0.0002314298],"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":[0.0002314986,0.0003064414,0.005667472,0.0001699012,0.00005372471,0.0002600702,0.000488441,0.7583671,0.01187514,0.1869697,0.008999547,0.02661096],"study_design_scores_gemma":[0.00001652064,0.00002047765,0.0003626186,0.000006175066,0.000002684065,0.00001432808,0.00002727239,0.9876189,0.001038761,0.01031642,0.000566071,0.000009773529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3883929,0.0002613354,0.5841035,0.002504936,0.0003199103,0.0003803344,0.001093175,0.002354811,0.02058917],"genre_scores_gemma":[0.9267604,0.000172787,0.06627149,0.0004789524,0.00007292102,0.0004056157,0.0004997154,0.0007302458,0.004607856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008300621,"threshold_uncertainty_score":0.01994401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01961122068444576,"score_gpt":0.2781370263562916,"score_spread":0.2585258056718459,"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."}}