{"id":"W1815810516","doi":"10.1002/jwmg.1003","title":"Quantifying regional variation in population trends using migrating counts","year":2015,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Environment and Climate Change Canada; Birds Canada; Acadia University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Bird Studies Canada","keywords":"Akaike information criterion; Population; Replicate; Regional variation; Geography; Trend analysis; Selection (genetic algorithm); Statistics; Population size; Population model; Population projection; Variation (astronomy); Population growth; Demography; Mathematics; Computer science","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":[0.005217742,0.0006020853,0.0004673688,0.001865675,0.0002983775,0.00102972,0.000635391,0.0003879967,0.0009513076],"category_scores_gemma":[0.01205543,0.0002900336,0.001122556,0.001381019,0.0003121012,0.001096169,0.0006364199,0.0005779138,0.0002097139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006333173,"about_ca_system_score_gemma":0.0005173461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01468355,"about_ca_topic_score_gemma":0.01751608,"domain_scores_codex":[0.9979939,0.001032763,0.0001350193,0.0005077306,0.0002098687,0.00012068],"domain_scores_gemma":[0.9918842,0.005062028,0.001260492,0.0007679048,0.0008923531,0.0001330126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001967878,0.00007414442,0.7537153,0.00008852836,0.0008609829,0.00007582615,0.00027318,0.2140995,0.003737914,0.001276131,0.000428021,0.02517375],"study_design_scores_gemma":[0.00001271261,0.0002486724,0.3745611,0.00003581296,0.000215234,0.0001033879,0.0003186881,0.6192454,0.00226557,0.002090879,0.0008532124,0.00004940511],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9163743,0.0001833641,0.08067941,0.00004525669,0.00001131089,0.00007219807,0.0009743288,0.0002707865,0.001389012],"genre_scores_gemma":[0.9859716,0.00004526016,0.01310025,0.00001106793,0.000004611775,0.00003315077,0.0006718937,0.00002944644,0.00013273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01468355,"threshold_uncertainty_score":0.02919614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0840599265323904,"score_gpt":0.3132753773038981,"score_spread":0.2292154507715077,"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."}}