{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001516187,0.0002363948,0.0003020601,0.00001290884,0.00170595,0.00009612912,0.0002608675,0.00008681523,0.001647845],"category_scores_gemma":[0.0002241938,0.0002183228,0.00003253471,0.00004686159,0.0006694545,0.0002204538,0.002999551,0.0002930729,0.00004601326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002830307,"about_ca_system_score_gemma":0.00003573349,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6872542,"about_ca_topic_score_gemma":0.9843864,"domain_scores_codex":[0.9986494,0.00008550045,0.0002156443,0.0004683397,0.0002080256,0.0003730863],"domain_scores_gemma":[0.9991734,0.00007125474,0.0002174061,0.0003927545,0.0000211271,0.0001240484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001214112,0.00005879633,0.9727811,0.00001337674,0.00004570056,0.00006062007,0.00003481454,0.00001417667,0.000004610223,0.00009805183,0.002608091,0.02415928],"study_design_scores_gemma":[0.0003214832,0.0002942962,0.9315256,0.000006713266,0.00003633887,0.00002624655,0.00006706443,0.0003124451,0.00004428671,0.0002134611,0.06696546,0.0001865574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990658,0.00003761483,0.000002103707,0.003133253,0.0002779483,0.0002769877,0.00008867453,0.0001182517,0.005407125],"genre_scores_gemma":[0.9974335,0.0003482009,0.0001956397,0.00008142423,0.00006854083,0.000057899,0.0001869335,0.00002149497,0.001606348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2971323,"threshold_uncertainty_score":0.9995937,"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."}}