{"id":"W4236150232","doi":"10.1016/j.bpj.2010.12.1949","title":"Using Entropy Leads to a Better Understanding of Biological Systems","year":2011,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Entropy (arrow of time); Randomness; Computer science; Acknowledgement; Principle of maximum entropy; Statistical physics; Theoretical computer science; Computational biology; Management science; Data science; Mathematics; Biology; Artificial intelligence; Physics; Engineering; Statistics","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":[0.001004673,0.0007861287,0.001145078,0.001229933,0.0004785401,0.001798331,0.0007352371,0.0009678782,0.001994989],"category_scores_gemma":[0.003811639,0.000398495,0.0008776017,0.0004846065,0.00155749,0.005756517,0.00149993,0.001525552,0.0002792429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006548517,"about_ca_system_score_gemma":0.0006897016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007368979,"about_ca_topic_score_gemma":0.0008417392,"domain_scores_codex":[0.9995346,0.0001813543,0.00003127724,0.00007477932,0.0001431413,0.00003495323],"domain_scores_gemma":[0.9981184,0.001313419,0.0001605492,0.0002736231,0.000077921,0.0000561502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001290657,0.0001458514,0.006851912,0.0006991993,0.0002852629,0.0002067523,0.0003322931,0.4769424,0.02710965,0.4317527,0.00144681,0.05409802],"study_design_scores_gemma":[0.00001647495,0.00004543511,0.001084269,0.0000296562,0.00002946111,0.00007414287,0.00004511994,0.494621,0.003236722,0.4985591,0.002223504,0.00003514715],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.241313,0.006052048,0.7296434,0.004062882,0.0003523363,0.00005753482,0.0005368722,0.0005051954,0.01747664],"genre_scores_gemma":[0.9424125,0.00422568,0.05063818,0.000342403,0.0002995411,0.00008242905,0.0002713507,0.0001086334,0.00161932],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001994989,"threshold_uncertainty_score":0.006673932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2331266129866508,"score_gpt":0.3447865121488357,"score_spread":0.1116598991621849,"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."}}