{"id":"W4206903088","doi":"10.1101/2022.01.20.477098","title":"Image-centric compression of protein structures improves space savings","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lossless compression; Huffman coding; Computer science; Data compression; Image compression; Compression (physics); Compression ratio; Image file formats; Encoding (memory); Computational science; File size; Gas compressor; File format; Image (mathematics); Computer graphics (images); Algorithm; Theoretical computer science; Computer engineering; Computer vision; Image processing; Artificial intelligence; Database","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":[0.0003237928,0.0007141927,0.0004712676,0.0009197136,0.0002513101,0.0008674692,0.001527046,0.0006006901,0.004436405],"category_scores_gemma":[0.001727744,0.0001824704,0.0002730781,0.001416633,0.00046042,0.001270566,0.0007423975,0.0005225173,0.001348458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005823852,"about_ca_system_score_gemma":0.0004217644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00182814,"about_ca_topic_score_gemma":0.001199848,"domain_scores_codex":[0.9995727,0.00002621284,0.00002504865,0.00007226947,0.0002493952,0.00005434519],"domain_scores_gemma":[0.9990497,0.0002569838,0.00008512008,0.0002336332,0.0003252432,0.00004929709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001314486,0.0003356268,0.004051308,0.0006748968,0.00008458109,0.0006767555,0.000347778,0.04401616,0.3663588,0.007131084,0.03665433,0.5383542],"study_design_scores_gemma":[0.00009562715,0.0002730008,0.002804222,0.00005078749,0.000041926,0.0006370837,0.00007876584,0.3437366,0.6336818,0.001714991,0.01683241,0.00005292548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5083333,0.00388021,0.4168999,0.00143273,0.000411681,0.000256466,0.002323571,0.05031649,0.01614559],"genre_scores_gemma":[0.7480541,0.001148731,0.2400098,0.0004386213,0.000125372,0.0001296107,0.003709714,0.001617265,0.00476673],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004436405,"threshold_uncertainty_score":0.01484126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009245792001998163,"score_gpt":0.2183854240670093,"score_spread":0.2091396320650112,"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."}}