{"id":"W4300817977","doi":"10.1155/2022/1839946","title":"Molecular Characterization and In Silico Analyses of Maurolipin Structure as a Secretory Phospholipase <a:math xmlns:a=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"> <a:msub> <a:mrow> <a:mi>A</a:mi> </a:mrow> <a:mrow> <a:mn>2</a:mn> </a:mrow> </a:msub> </a:math> (<c:math xmlns:c=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\"> <c:msub> <c:mrow> <c:mtext>sPLA</c:mtext> </c:mrow> <c:mrow> <c:mn>2</c:mn> </c:mrow> </c:msub> </c:math>) from Venom Glands of Iranian Scorpio maurus (Arachnida: Scorpionida)","year":2022,"lang":"lv","type":"article","venue":"Journal of Tropical Medicine","topic":"Venomous Animal Envenomation and Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Shiraz University; Shiraz University of Medical Sciences","keywords":"In silico; Computational biology; Characterization (materials science); Computer science; Biology; Physics; Biochemistry; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.003077015,0.003591495,0.005394565,0.002091607,0.001818688,0.0005315941,0.003577469,0.002791346,0.001666547],"category_scores_gemma":[0.002242707,0.003651777,0.002491425,0.002659326,0.002976609,0.0007784244,0.002768123,0.005057746,0.0002029274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006829746,"about_ca_system_score_gemma":0.002537714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001000106,"about_ca_topic_score_gemma":0.0004212798,"domain_scores_codex":[0.9775556,0.001933375,0.007781616,0.00381549,0.005267558,0.003646336],"domain_scores_gemma":[0.9837315,0.0009437518,0.008386738,0.003445974,0.001241045,0.002250961],"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":[0.01365951,0.00427618,0.004218114,0.003869908,0.005086902,0.004230717,0.00612,0.001655124,0.871389,0.07615065,0.002722547,0.006621335],"study_design_scores_gemma":[0.05661495,0.03931076,0.1265718,0.01218211,0.01441864,0.01130176,0.01790571,0.2878717,0.3056882,0.000986084,0.1142605,0.01288766],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9647067,0.02065923,0.001981542,0.002714753,0.002973917,0.001322656,0.004280686,0.0002261941,0.001134351],"genre_scores_gemma":[0.9793531,0.007492428,0.001548536,0.0029837,0.003191541,0.0003481653,0.003571293,0.0008210336,0.0006901539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5657008,"threshold_uncertainty_score":0.9997367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615692411004692,"score_gpt":0.2561118086977711,"score_spread":0.2399548845877242,"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."}}