{"id":"W3048687722","doi":"10.1111/ddi.13129","title":"Predicting spatiotemporal abundance of breeding waterfowl across Canada: A Bayesian hierarchical modelling approach","year":2020,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Ducks Unlimited Canada; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Waterfowl; Abundance (ecology); Bayesian probability; Habitat; Ecology; Generalized linear model; Generalized additive model; Statistics; Geography; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008120853,0.000088275,0.0001229597,0.000003309365,0.001107835,0.00001889643,0.0001430736,0.00004667076,0.0006963834],"category_scores_gemma":[0.0000280732,0.00009090002,0.00003714663,0.000175492,0.0002222777,0.0001409763,0.0008611215,0.0001211939,0.000005004774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002247633,"about_ca_system_score_gemma":0.00001462931,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1576837,"about_ca_topic_score_gemma":0.0218933,"domain_scores_codex":[0.9991559,0.00001835598,0.0001334306,0.0002223738,0.0002354891,0.0002345011],"domain_scores_gemma":[0.9996367,0.000018145,0.0000572516,0.00007134662,0.000015005,0.0002015185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004665865,0.0000860184,0.9883898,0.00004848954,0.00001907616,0.000006246708,0.004025811,0.00298633,0.0001979691,0.002031017,0.001874968,0.0002875612],"study_design_scores_gemma":[0.001472995,0.0001610548,0.5697275,0.00002817514,0.00008079933,0.0000141027,0.01853885,0.399833,0.001404739,0.0002132921,0.007897825,0.0006276994],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9310227,0.00001474711,0.0643959,0.0009989225,0.00002949938,0.00008003063,0.001924974,0.00002613744,0.001507083],"genre_scores_gemma":[0.9993145,0.00001213775,0.0002150353,0.0001293325,0.00001833247,0.000001649239,0.0002949078,0.000002530456,0.00001156333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4186624,"threshold_uncertainty_score":0.9959546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04669552686835117,"score_gpt":0.2162519115927466,"score_spread":0.1695563847243954,"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."}}