{"id":"W3025881171","doi":"10.1093/condor/duaa007","title":"Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada","year":2020,"lang":"en","type":"article","venue":"Ornithological Applications","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Environment and Climate Change Canada; Alberta Biodiversity Monitoring Institute; University of Alberta","funders":"U.S. Fish and Wildlife Service; Environment and Climate Change Canada; Western Canada Research Grid; Compute Canada; Alberta Biodiversity Monitoring Institute","keywords":"Breeding bird survey; Population; Boreal; Habitat; Geography; Abundance (ecology); Estimator; Population size; Ecology; Mark and recapture; Statistics; Biology; Demography; Mathematics","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":[0.010711,0.0008971714,0.000620171,0.0009832489,0.0008565926,0.002088887,0.002649401,0.0009701761,0.001215765],"category_scores_gemma":[0.03154081,0.0004696124,0.0007671421,0.001225066,0.001119361,0.001654282,0.001273095,0.0007887881,0.0001189252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008090136,"about_ca_system_score_gemma":0.007371161,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8784679,"about_ca_topic_score_gemma":0.8471823,"domain_scores_codex":[0.9967361,0.00227413,0.0001397036,0.0004124604,0.0002883318,0.0001492023],"domain_scores_gemma":[0.9850322,0.01209155,0.0005114789,0.0006690557,0.001434669,0.0002610011],"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":[0.00009490475,0.00005555801,0.1096791,0.0000868378,0.0003325216,0.0001349307,0.0005353384,0.8453444,0.0002320923,0.010422,0.001403092,0.03167919],"study_design_scores_gemma":[0.00002555195,0.00004145419,0.02296181,0.00004592343,0.00007217511,0.00002823094,0.0004254807,0.968688,0.0001066762,0.006637464,0.0009303513,0.00003697047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8910961,0.002013234,0.09434523,0.004397245,0.0001121648,0.0001039032,0.0007441037,0.0004110566,0.006776903],"genre_scores_gemma":[0.975952,0.0003370405,0.02228724,0.0002411223,0.00004724339,0.00003462745,0.0002733405,0.0000605172,0.0007669515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1215321,"threshold_uncertainty_score":0.2444957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0638868387893436,"score_gpt":0.2871292471663422,"score_spread":0.2232424083769987,"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."}}