{"id":"W7055408092","doi":"","title":"Ãcologie moléculaire et phylogéographie des ciscos d'Amérique du Nord (Teleostei: Coregonus spp.)","year":2000,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Laser Design and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Coregonus; Population; Statistical analysis; Selection (genetic algorithm)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004459428,0.0004312026,0.0004224044,0.004873972,0.001728699,0.001789988,0.0004606158,0.0006007092,0.01593664],"category_scores_gemma":[0.0007327338,0.0002973225,0.0003138806,0.004855928,0.000704446,0.0008512128,0.0005866779,0.0009473076,0.004079974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003620715,"about_ca_system_score_gemma":0.00472226,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3075682,"about_ca_topic_score_gemma":0.3155474,"domain_scores_codex":[0.9997055,0.00003139515,0.000009840125,0.00008463515,0.0001329658,0.000035611],"domain_scores_gemma":[0.9993766,0.000125102,0.0000861559,0.00004563834,0.0003088497,0.00005780076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007519046,0.00006741241,0.2102042,0.001334125,0.00008939266,0.000806366,0.00371215,0.002689438,0.1422288,0.01302808,0.02203558,0.6030526],"study_design_scores_gemma":[0.00002057598,0.00006848287,0.4603243,0.0002430669,0.00003530673,0.0009714906,0.001981433,0.001376837,0.01945313,0.00152874,0.513958,0.00003858121],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.579946,0.04877907,0.03117573,0.002425685,0.0005775519,0.0003106411,0.0373565,0.001358621,0.2980702],"genre_scores_gemma":[0.6825917,0.02748652,0.04111582,0.0005466321,0.0001516814,0.0002859678,0.01884088,0.001431751,0.227549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6924318,"threshold_uncertainty_score":0.6115559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004732275014146516,"score_gpt":0.144731331888017,"score_spread":0.1399990568738704,"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."}}