{"id":"W2567760054","doi":"10.3354/meps12030","title":"Predictions from machine learning ensembles: marine bird distribution and density on Canada’s Pacific coast","year":2017,"lang":"en","type":"article","venue":"Marine Ecology Progress Series","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Raincoast Conservation Foundation; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Raincoast Conservation Foundation; California Sea Grant, University of California, San Diego; Environment and Climate Change Canada; Vancouver Foundation; Marisla Foundation; Gordon and Betty Moore Foundation; McLean Foundation; Bullitt Foundation","keywords":"Geography; Arctic; Marine ecosystem; Oceanography; Wildlife; Ecology; Habitat; Conservation biology; Threatened species; Biota; Citizen science; Biodiversity; Distribution (mathematics); Ecosystem; Fishery; Biology; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001189214,0.0005814321,0.0004007721,0.0006324128,0.0005274249,0.0005987064,0.0008711196,0.0004105207,0.0007988506],"category_scores_gemma":[0.003392776,0.0002646319,0.0004969481,0.0007029323,0.0003245853,0.0003811553,0.0005178299,0.0006852376,0.0001676611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003655506,"about_ca_system_score_gemma":0.003790944,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8009161,"about_ca_topic_score_gemma":0.7740417,"domain_scores_codex":[0.9997582,0.0000552828,0.0000104118,0.000080433,0.0000486304,0.00004708761],"domain_scores_gemma":[0.9988045,0.0005402096,0.00008596594,0.00008810383,0.000393214,0.00008809349],"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":[0.0001185076,0.00004385835,0.08384133,0.00002180673,0.00009029393,0.0000501074,0.00007871461,0.890293,0.0003170593,0.0004089998,0.001995887,0.02274048],"study_design_scores_gemma":[0.000007471172,0.00001149158,0.01339062,0.000007308759,0.00001646971,0.000007140527,0.00003875256,0.9855561,0.0002216169,0.0003362451,0.0003991311,0.000007634056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851661,0.0002396705,0.009554085,0.000316676,0.00002181695,0.00003249761,0.002520895,0.0003651131,0.001783112],"genre_scores_gemma":[0.9905289,0.00009631814,0.004909605,0.00004857715,0.00000945733,0.00001987052,0.003716882,0.00002017793,0.0006502278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1990839,"threshold_uncertainty_score":0.4005126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009133093923662065,"score_gpt":0.2097316039969807,"score_spread":0.2005985100733186,"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."}}