{"id":"W6892300750","doi":"10.5061/dryad.4cj42","title":"Data from: Loss of genetic integrity in wild lake trout populations following stocking: insights from an exhaustive study of 72 lakes from Québec, Canada","year":2014,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Stocking; Genetic diversity; Trout; Context (archaeology); Population; Genetic monitoring; Genetic variability","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001129362,0.001184089,0.0008619871,0.004489348,0.002219535,0.00201176,0.002352516,0.000894325,0.0198048],"category_scores_gemma":[0.005255669,0.0004659547,0.000674708,0.009884954,0.0005747012,0.0006085641,0.001310192,0.0009691525,0.006083837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01668253,"about_ca_system_score_gemma":0.03202837,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9854977,"about_ca_topic_score_gemma":0.9921555,"domain_scores_codex":[0.999236,0.00004669151,0.00007900957,0.0001480252,0.0002924154,0.0001978764],"domain_scores_gemma":[0.9958804,0.0004272176,0.0003441855,0.0004184461,0.002577904,0.0003519676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001862559,0.00004375303,0.0462959,0.001085064,0.0001931857,0.0001544241,0.0005496701,0.001185374,0.0005219862,0.001030491,0.9341996,0.01455432],"study_design_scores_gemma":[0.0002923756,0.0000229316,0.3563161,0.0009038891,0.0001356192,0.0001057298,0.0009686523,0.002044671,0.0008704105,0.0007324344,0.6374705,0.0001366605],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00212225,0.00009389944,0.0001013441,0.0000856719,0.000008104783,0.00002465027,0.9965221,0.0001443834,0.0008975834],"genre_scores_gemma":[0.006708793,0.0001317147,0.000625557,0.00003980898,0.000003862363,0.0001082513,0.9909473,0.00005214708,0.001382589],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0198048,"threshold_uncertainty_score":0.1210408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05073535112434476,"score_gpt":0.3014250655478622,"score_spread":0.2506897144235175,"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."}}