{"id":"W2883553124","doi":"10.1186/s13071-018-3005-3","title":"Identification and preliminary characterization of Hc-clec-160, a novel C-type lectin domain-containing gene of the strongylid nematode Haemonchus contortus","year":2018,"lang":"en","type":"article","venue":"Parasites & Vectors","topic":"Helminth infection and control","field":"Veterinary","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Institute of Genetics and Developmental Biology, Chinese Academy of Sciences; Chinese Academy of Sciences; State Key Laboratory of Veterinary Etiological Biology; Institute of Genetics; National Natural Science Foundation of China","keywords":"Haemonchus contortus; Biology; Caenorhabditis elegans; Gene knockdown; RNA interference; Nematode; Gene; Exon; C-type lectin; Molecular biology; Parasitology; Genetics; RNA; Lectin; Zoology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002959719,0.0001749562,0.0002951194,0.0001058363,0.0001705353,0.00003244056,0.000156775,0.000104977,0.0001139404],"category_scores_gemma":[0.0001711981,0.0001477666,0.0000834762,0.0002978681,0.0001842573,0.0002089285,0.00006094051,0.0001083081,0.00001610303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004701501,"about_ca_system_score_gemma":0.00006497057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000205531,"about_ca_topic_score_gemma":0.000050686,"domain_scores_codex":[0.9986357,0.000127669,0.0005321101,0.0002784099,0.0002074509,0.0002186799],"domain_scores_gemma":[0.9986978,0.0001104659,0.0005059192,0.0003651477,0.0002571199,0.0000635583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006183191,0.00009598141,0.1161798,0.00003964592,0.00006855986,0.000001487328,0.001367211,0.000002136503,0.8800326,0.0002630278,0.0000176398,0.001313611],"study_design_scores_gemma":[0.0006776464,0.000894447,0.8156573,0.00006274377,0.00006802785,0.00006574421,0.0001474793,0.0009059728,0.1809435,0.00003325046,0.0003963972,0.0001474389],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952073,0.00007480681,0.003017908,0.00007964095,0.0007425918,0.0004809944,0.00001578942,0.00004850502,0.0003324545],"genre_scores_gemma":[0.999433,0.00000875251,0.00009266355,0.00005642482,0.0001503694,0.00002848802,0.00002232841,0.00002521032,0.0001827603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6994776,"threshold_uncertainty_score":0.6025749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02920800179846479,"score_gpt":0.3021603844375124,"score_spread":0.2729523826390476,"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."}}