{"id":"W1975155217","doi":"10.1007/s10126-010-9285-z","title":"An Integrated Approach to Gene Discovery and Marker Development in Atlantic Cod (Gadus morhua)","year":2010,"lang":"en","type":"article","venue":"Marine Biotechnology","topic":"Aquaculture disease management and microbiota","field":"Immunology and Microbiology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Memorial University of Newfoundland; Université Sainte-Anne; Institute for Marine Biosciences","funders":"Division of Ocean Sciences; Genome Canada; Fisheries and Oceans Canada; Genome Atlantic; Atlantic Canada Opportunities Agency; McGill University","keywords":"Biology; Atlantic cod; Gadus; Genomics; Domestication; Aquaculture; Selective breeding; Biotechnology; Computational biology; Gene; Fishery; Molecular marker; Molecular breeding; Genetics; Genome; Evolutionary biology; Fish <Actinopterygii>","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005007564,0.0003832881,0.0004464035,0.0007091117,0.0002892579,0.0006817754,0.000423147,0.0003552296,0.0009989286],"category_scores_gemma":[0.0004724911,0.0003515937,0.0005233058,0.0006143911,0.0002820819,0.0003191604,0.001081822,0.0008224667,0.0005115594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003606879,"about_ca_system_score_gemma":0.0007067921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001499776,"about_ca_topic_score_gemma":0.00355503,"domain_scores_codex":[0.999567,0.0000434309,0.00003714413,0.000175019,0.000120101,0.00005732807],"domain_scores_gemma":[0.99977,0.00005148848,0.00005461818,0.00004228563,0.00004183653,0.0000398561],"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":[0.00009834699,0.00004238557,0.001089063,0.00005712323,0.00001674334,0.00007351246,0.00005535027,0.0002497392,0.980349,0.0002472214,0.00009366462,0.01762781],"study_design_scores_gemma":[0.0001240275,0.001109548,0.0388451,0.0000477329,0.0003039438,0.001116596,0.000284373,0.008975043,0.9252286,0.0007868995,0.02313002,0.00004824593],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7791533,0.001414589,0.2097809,0.0004681031,0.0000938585,0.0004714978,0.003332376,0.001001676,0.004283741],"genre_scores_gemma":[0.6164626,0.002171722,0.3540017,0.0006514374,0.00004592455,0.0006155501,0.01386242,0.0003733137,0.01181532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001499776,"threshold_uncertainty_score":0.003341675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005168418186707655,"score_gpt":0.2010965877635443,"score_spread":0.1959281695768367,"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."}}