{"id":"W4414125555","doi":"10.31223/x5r444","title":"Added value of a priori bias correcting dynamically downscaled data for application to species distribution models - a case study for coastal British Columbia","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"A priori and a posteriori; Climate change; Climate model; Reliability (semiconductor); Climate system; Distribution (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001575317,0.0005990269,0.000391152,0.0005796783,0.0006788989,0.001182785,0.0009371717,0.0006232517,0.001482073],"category_scores_gemma":[0.008328265,0.0003570964,0.0004486465,0.001386239,0.0003330476,0.0007244774,0.0006978894,0.0008262013,0.0002100268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001803269,"about_ca_system_score_gemma":0.003360558,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.425083,"about_ca_topic_score_gemma":0.455842,"domain_scores_codex":[0.999482,0.0001790883,0.00004736768,0.0001081181,0.000135688,0.00004771852],"domain_scores_gemma":[0.9965152,0.001126529,0.000260813,0.0006941358,0.001282128,0.0001210916],"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":[0.0002529178,0.0001437152,0.09376251,0.0000948413,0.0002345308,0.0002486687,0.0001725883,0.8601258,0.00560539,0.0009478271,0.002635641,0.03577553],"study_design_scores_gemma":[0.000148889,0.00004940857,0.08176918,0.00005273317,0.00007515694,0.00005684788,0.00018215,0.9056932,0.006404826,0.0009924636,0.004476904,0.00009831214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824733,0.0002480926,0.008351228,0.0004565612,0.0000610504,0.00004859144,0.004171311,0.0011919,0.002998013],"genre_scores_gemma":[0.9776397,0.0001284764,0.01763795,0.00008449944,0.00001392377,0.00003824324,0.003662982,0.0002315906,0.0005626404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.574917,"threshold_uncertainty_score":0.8452175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06583294870154859,"score_gpt":0.3079907493705088,"score_spread":0.2421578006689602,"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."}}