{"id":"W4300987943","doi":"","title":"Demographic response of Atlantic salmon populations to multiple stressors : a Bayesian hierarchical modelling approach at broad ocean scale","year":2013,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Scale (ratio); Bayesian probability; Computer science; Environmental science; Econometrics; Oceanography; Fishery; Geography; Artificial intelligence; Biology; Mathematics; Geology; Cartography","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":[0.002008379,0.0003909145,0.0008202318,0.0005763401,0.0006047877,0.0009741798,0.001310223,0.0009365692,0.006027217],"category_scores_gemma":[0.00639302,0.0005955115,0.001533092,0.0006398451,0.0006562624,0.001047212,0.0009520316,0.0009467396,0.0004881434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006388671,"about_ca_system_score_gemma":0.0008759333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03514124,"about_ca_topic_score_gemma":0.0299953,"domain_scores_codex":[0.9995869,0.0001878808,0.00002443748,0.0001245643,0.00003322476,0.00004305175],"domain_scores_gemma":[0.9982066,0.001360544,0.0001397525,0.0001209816,0.00008026299,0.0000918723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005002159,0.00002756565,0.01300795,0.00003587881,0.0001604744,0.00006374952,0.0001681071,0.9667863,0.0007075262,0.006175316,0.0006929861,0.01212421],"study_design_scores_gemma":[0.000009982003,0.0000171752,0.009949409,0.00001545665,0.00003418476,0.00001837548,0.00003278389,0.9820414,0.00005620065,0.00755458,0.0002519642,0.00001854087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6911189,0.0004006642,0.30135,0.001541362,0.00006850828,0.0000554707,0.001880089,0.000269182,0.003315895],"genre_scores_gemma":[0.9714401,0.0002841633,0.02263657,0.0001460939,0.00007985879,0.0001060048,0.001207193,0.00009330226,0.004006689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03514124,"threshold_uncertainty_score":0.06987339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02346611982745855,"score_gpt":0.2345989009692729,"score_spread":0.2111327811418143,"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."}}