{"id":"W4403449165","doi":"10.1093/ornithapp/duae055","title":"Improving bird abundance estimates in harvested forests with retention by limiting detection radius through sound truncation","year":2024,"lang":"en","type":"article","venue":"Ornithological applications","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Biodiversity Monitoring Institute; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Limiting; Abundance (ecology); Truncation (statistics); RADIUS; Environmental science; Sound (geography); Relative species abundance; Mathematics; Statistics; Ecology; Acoustics; Physics; Biology; Computer science; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002296576,0.0001753086,0.0001367328,0.0000492075,0.0003171943,0.0001848961,0.0001534389,0.000124477,0.00005342703],"category_scores_gemma":[0.00005252623,0.0001405196,0.00004360676,0.0009332231,0.0002533503,0.0003644845,0.00005057088,0.0002721701,0.0004604931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189929,"about_ca_system_score_gemma":0.00001076108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001255111,"about_ca_topic_score_gemma":0.001142877,"domain_scores_codex":[0.9985768,0.0000473391,0.0002863265,0.0006130044,0.0001979229,0.0002786201],"domain_scores_gemma":[0.9993526,0.0001865607,0.00009428946,0.0002969907,0.00001587537,0.00005366641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005807467,0.0004425869,0.1315022,0.0001054735,0.00002732215,0.00001654732,0.0006333315,0.004453427,0.3695493,0.001757597,0.0007347816,0.4907194],"study_design_scores_gemma":[0.0008188701,0.0003827208,0.8282418,0.0001656911,0.0001416208,0.0003819106,0.0003599353,0.06048312,0.02495765,0.0475368,0.03535311,0.001176785],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4509159,0.0001114565,0.5456747,0.0004166803,0.00002330132,0.0005810902,0.000007777844,0.0004872131,0.001781855],"genre_scores_gemma":[0.978541,0.00002031026,0.02068354,0.00007639274,0.00004524691,0.0003327009,0.00005874311,0.00003355414,0.0002085155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6967396,"threshold_uncertainty_score":0.591886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539127629596791,"score_gpt":0.2460015190414717,"score_spread":0.2306102427455038,"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."}}