{"id":"W3189679040","doi":"10.1002/ecs2.3698","title":"Narwhal (<i>Monodon monoceros</i>) detection by infrared flukeprints from aerial survey imagery","year":2021,"lang":"en","type":"article","venue":"Ecosphere","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of British Columbia; Fisheries and Oceans Canada","funders":"Natural Resources Canada; Fisheries and Oceans Canada; Environment and Climate Change Canada","keywords":"Remote sensing; Visibility; Aerial photography; Wildlife; Environmental science; Aerial survey; Infrared; Geology; Geography; Ecology; Biology; Meteorology; Optics; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001769584,0.0001992593,0.0001167029,0.0006481826,0.0002656643,0.0003431166,0.0001394643,0.0001632714,0.002395008],"category_scores_gemma":[0.0002862121,0.00008620667,0.00009619978,0.0002260304,0.0001790787,0.0002650091,0.0002892347,0.0001557561,0.0002823058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000258973,"about_ca_system_score_gemma":0.0001143021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01279731,"about_ca_topic_score_gemma":0.05832845,"domain_scores_codex":[0.9999176,0.00001047292,0.000004779761,0.00003454716,0.00002011073,0.00001239217],"domain_scores_gemma":[0.9997326,0.00003693796,0.0001062002,0.00001386429,0.00006498722,0.00004548259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003038596,0.0000539306,0.7597026,0.000259802,0.00008472936,0.0003689261,0.00127611,0.0002467199,0.1879877,0.0001403947,0.000915317,0.04865986],"study_design_scores_gemma":[0.000002178334,0.000128261,0.9890991,0.00002871883,0.00002866383,0.0001852794,0.0007295315,0.0002858651,0.007650957,0.0000358997,0.001818798,0.000006746725],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994245,0.0002700643,0.0007634151,0.00005256207,0.00001213923,0.00002320067,0.0003957534,0.00002340159,0.004214602],"genre_scores_gemma":[0.9953951,0.0001820883,0.002235196,0.00005625542,0.00001037376,0.00002012594,0.000259114,0.000005369305,0.001836333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01279731,"threshold_uncertainty_score":0.0254457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255027919576456,"score_gpt":0.209852531560232,"score_spread":0.1973022523644675,"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."}}