{"id":"W2094372888","doi":"10.1016/j.vascn.2012.11.003","title":"Troubleshooting the rabbit ferric chloride-induced arterial model of thrombosis to assess in vivo efficacy of antithrombotic drugs","year":2012,"lang":"en","type":"article","venue":"Journal of Pharmacological and Toxicological Methods","topic":"Barrier Structure and Function Studies","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Antithrombotic; In vivo; Pharmacology; Thrombosis; Ferric; Troubleshooting; Medicine; Rabbit (cipher); Chemistry; Cardiology; Internal medicine; Biology; Biotechnology; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.002517702,0.000218834,0.0008865675,0.0001473964,0.000141557,0.00002325694,0.0003618671,0.0001609104,0.0001987006],"category_scores_gemma":[0.002226077,0.0001109638,0.0002063878,0.0005771612,0.0002714792,0.0002135025,0.0002511129,0.0005553233,6.933433e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004035577,"about_ca_system_score_gemma":0.00003900236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001840609,"about_ca_topic_score_gemma":2.898691e-7,"domain_scores_codex":[0.9968871,0.001141772,0.0009560628,0.0002383809,0.000365891,0.0004107239],"domain_scores_gemma":[0.9958374,0.003127449,0.0005938179,0.0001000231,0.0001063166,0.0002350168],"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":[0.0006279095,0.0004928863,0.001408561,0.00001489324,0.00004835484,0.0000039418,0.0005813502,0.0004544734,0.9913788,0.001162611,0.0000518472,0.003774338],"study_design_scores_gemma":[0.001696366,0.001012247,0.04734027,0.00002383145,0.0002054832,0.0000369575,0.0002893657,0.0009431876,0.9466689,0.001506423,0.0001156001,0.0001614152],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895855,0.00009102191,0.007737421,0.0006532107,0.00112452,0.0002856152,0.000005306036,0.00001079051,0.0005066129],"genre_scores_gemma":[0.9851333,0.0001249761,0.01343933,0.0009556536,0.0003198326,0.000009760652,3.107584e-8,0.000007469836,0.000009650161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04593171,"threshold_uncertainty_score":0.4524973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2858173316865696,"score_gpt":0.4568083296536285,"score_spread":0.1709909979670589,"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."}}