{"id":"W4414695429","doi":"10.22541/au.175934402.26193196/v1","title":"Estimating spatiotemporal variation in mortality improves conservation insights","year":2025,"lang":"en","type":"article","venue":"","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Wetland and Waterfowl Research, Ducks Unlimited Canada; National Science Foundation","keywords":"Habitat; Population; Wildlife; Vital rates; Variation (astronomy); Wetland; Aerial survey; Climate change; Population growth","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003079025,0.0003885923,0.0004107716,0.002376848,0.0003090774,0.001313578,0.0005060247,0.0003904492,0.001533044],"category_scores_gemma":[0.01131329,0.0002733342,0.0006661006,0.002086922,0.0003346883,0.002502677,0.0008314034,0.0005435761,0.0002565973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004585753,"about_ca_system_score_gemma":0.0004527809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01481229,"about_ca_topic_score_gemma":0.02758046,"domain_scores_codex":[0.998874,0.0003750694,0.0001151247,0.0004746972,0.0001025776,0.00005851192],"domain_scores_gemma":[0.9946189,0.002381078,0.001812008,0.0007350976,0.0003467553,0.00010617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003484153,0.00006394785,0.9076177,0.00009905513,0.0004877845,0.00005373765,0.0002434713,0.01845527,0.0009973568,0.001715592,0.0009894128,0.06924183],"study_design_scores_gemma":[0.000007598174,0.00009022604,0.8818465,0.00009865921,0.0002115716,0.0001267625,0.0008217482,0.1026262,0.0006870574,0.009032204,0.004406609,0.00004484229],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8488325,0.00213205,0.1359985,0.0009806962,0.00007266731,0.00005141181,0.006031593,0.0004400034,0.005460592],"genre_scores_gemma":[0.9868599,0.0004203606,0.01055225,0.00005847089,0.00004287256,0.00001646992,0.00175894,0.00002429805,0.0002664374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01481229,"threshold_uncertainty_score":0.02945215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05402258414542728,"score_gpt":0.3362155007123674,"score_spread":0.2821929165669402,"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."}}