{"id":"W2741890902","doi":"10.1002/ecs2.1884","title":"Quantifying marine mammal hotspots in British Columbia, Canada","year":2017,"lang":"en","type":"article","venue":"Ecosphere","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Raincoast Conservation Foundation; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; Raincoast Conservation Foundation; International Fund for Animal Welfare","keywords":"Geography; Marine mammal; Hotspot (geology); Marine protected area; Marine conservation; Biodiversity hotspot; Ecology; Biodiversity; Spatial ecology; Wildlife; Environmental resource management; Habitat; Environmental science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002837654,0.0004359403,0.0002980088,0.0034246,0.001659494,0.001240089,0.0009748255,0.0002630183,0.002060333],"category_scores_gemma":[0.001326519,0.0002340863,0.0002499231,0.004905142,0.0004726663,0.0002203293,0.0006999383,0.0002337352,0.000320682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01228095,"about_ca_system_score_gemma":0.01430185,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9957582,"about_ca_topic_score_gemma":0.998626,"domain_scores_codex":[0.9997268,0.00001979603,0.0000156107,0.0000597765,0.00009912157,0.00007879189],"domain_scores_gemma":[0.9988355,0.0001078442,0.0001099873,0.00003224521,0.0007409273,0.0001734333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008719333,0.00003498315,0.9414569,0.0002126779,0.00009856056,0.0002383184,0.0009486683,0.003235641,0.001550805,0.0003246154,0.006546485,0.04526512],"study_design_scores_gemma":[0.000005772411,0.00001042676,0.9899017,0.00008137304,0.00003277418,0.00005330867,0.002073291,0.003602923,0.0003094497,0.00007731729,0.003831868,0.00001980743],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750821,0.001617498,0.001186659,0.0002122809,0.00001801138,0.0001113866,0.01406742,0.0001282555,0.007576285],"genre_scores_gemma":[0.9900724,0.0007122618,0.001604197,0.00006483542,0.000003665117,0.00005263653,0.004226375,0.00001431704,0.003249387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01228095,"threshold_uncertainty_score":0.08910501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0180260490617333,"score_gpt":0.2232647030485158,"score_spread":0.2052386539867825,"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."}}