{"id":"W4294834082","doi":"10.1101/2022.09.05.506423","title":"Chorography and conformational dynamism of the Soluble Human Fibrinogen in solution","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Blood properties and coagulation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"IONICS Mass Spectrometry (Canada)","funders":"Biological and Environmental Research; Basic Energy Sciences; National Institute of General Medical Sciences; U.S. Department of Energy; National Heart, Lung, and Blood Institute; Office of Science; National Institutes of Health; Brookhaven National Laboratory; University of Houston","keywords":"Fibrinogen; Fibrin; Chemistry; Macromolecule; Biophysics; Protein structure; Crystallography; Negative stain; Thrombin; Dynamism; Flexibility (engineering); Biochemistry; Biology; Physics; Platelet","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00007609253,0.0001982152,0.0001104261,0.0001552884,0.0001540274,0.0002783415,0.0001716813,0.0002031496,0.0005872627],"category_scores_gemma":[0.00009917893,0.0001185623,0.0001316526,0.00009724987,0.0003629091,0.0002835674,0.0001627316,0.0002495024,0.0001176768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002731018,"about_ca_system_score_gemma":0.0001538309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008459066,"about_ca_topic_score_gemma":0.0004496912,"domain_scores_codex":[0.9999647,0.000004997701,0.000001694128,0.0000132971,0.000009127545,0.000006163338],"domain_scores_gemma":[0.9999647,0.000009935211,0.00001130513,0.000004041939,0.000004365171,0.000005524686],"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":[0.0001151671,0.00002715408,0.00187813,0.00005643024,0.00001926608,0.0001847635,0.0001000996,0.01031526,0.9806094,0.003404494,0.0001641114,0.003125726],"study_design_scores_gemma":[0.00006098304,0.0005144579,0.01533811,0.00002899363,0.00005097589,0.0008175958,0.0004092098,0.4203564,0.5491101,0.009032505,0.004208765,0.00007193533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850054,0.0005486875,0.01279579,0.000134106,0.00001124964,0.0000072654,0.00008556192,0.00004765922,0.001364306],"genre_scores_gemma":[0.9967912,0.0003515749,0.002229669,0.0000228443,0.000004444,0.000007385622,0.00009268819,0.000006192318,0.0004940486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008459066,"threshold_uncertainty_score":0.001981556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0154253575491798,"score_gpt":0.2230191296021611,"score_spread":0.2075937720529813,"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."}}