{"id":"W3021546401","doi":"","title":"PHYSIOLOGICAL INDICATORS OF GROWTH: HOW GENOMICS CAN HELP US TO BETTER UNDERSTAND CROSS x TRAIT IN COMMERCIAL FISHES?","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Growth Hormone and Insulin-like Growth Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Growth hormone receptor; Biology; Somatostatin; Population; Salvelinus; Context (archaeology); Trait; Endocrinology; Growth hormone; Evolutionary biology; Internal medicine; Fishery; Hormone; Demography; Trout; Medicine; Fish <Actinopterygii>; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002259387,0.0006087915,0.0008587083,0.0007672997,0.0003769015,0.001317529,0.000612995,0.001553876,0.004666334],"category_scores_gemma":[0.001857938,0.0001810875,0.0002983152,0.0007243938,0.001616389,0.002059833,0.0007617892,0.00135066,0.000771963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048379,"about_ca_system_score_gemma":0.0006461439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003296343,"about_ca_topic_score_gemma":0.004177415,"domain_scores_codex":[0.9995943,0.00009340969,0.00002134833,0.0001650222,0.00007430478,0.00005160374],"domain_scores_gemma":[0.9987624,0.0004951264,0.0002057679,0.00009764198,0.0002463518,0.0001927711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009212803,0.000235813,0.1517324,0.0006154651,0.0002732094,0.0005880218,0.001117048,0.001105439,0.561297,0.01629219,0.007211396,0.2586107],"study_design_scores_gemma":[0.00004089964,0.0008382227,0.898479,0.0002844068,0.0001787849,0.0007263392,0.003480794,0.002254586,0.0343272,0.02968339,0.02949252,0.0002138297],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8141513,0.03472884,0.05702453,0.05977199,0.001925997,0.00007693686,0.003749013,0.0005205548,0.02805084],"genre_scores_gemma":[0.9202612,0.01820289,0.02623633,0.01185894,0.0008969663,0.0001366669,0.001093465,0.000271634,0.02104191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004666334,"threshold_uncertainty_score":0.0156104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02492874763528531,"score_gpt":0.2487994780943455,"score_spread":0.2238707304590603,"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."}}