{"id":"W1750134442","doi":"10.1890/es14-00383.1","title":"Characterizing demographic parameters across environmental gradients: a case study with Ontario moose (<i>Alces alces</i>)","year":2015,"lang":"en","type":"article","venue":"Ecosphere","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; University of Alberta; University of Guelph","funders":"","keywords":"Abundance (ecology); Carrying capacity; Population; Ecology; Vital rates; Population growth; Habitat; Productivity; Geography; Ungulate; Overexploitation; Biology; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003410335,0.0002429398,0.000208875,0.00001281691,0.000342712,0.00006580225,0.0002209418,0.00009087242,0.001285127],"category_scores_gemma":[0.0000101239,0.000211023,0.00005344672,0.0001810931,0.0002351241,0.0007807068,0.0001750334,0.0002387846,0.0007744023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003650072,"about_ca_system_score_gemma":0.0000259825,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02677904,"about_ca_topic_score_gemma":0.3910019,"domain_scores_codex":[0.9984086,0.000106762,0.0002493224,0.0005032605,0.0002794945,0.0004525834],"domain_scores_gemma":[0.9992311,0.00003751298,0.0001492388,0.0003415066,0.000004326415,0.0002363376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001015024,0.0005159608,0.9851189,0.000001389395,0.0000574312,0.001341755,0.01002657,0.0001916295,0.00003079999,7.339416e-7,0.001202155,0.001411189],"study_design_scores_gemma":[0.001419765,0.0008859705,0.9773145,0.00000731607,0.00004856274,0.000846348,0.01645946,0.000105815,0.00003082771,0.00004756505,0.002512733,0.0003211817],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977141,0.00002124695,0.0000226357,0.0001183774,0.000188858,0.0005387775,0.000009565857,0.00005852717,0.001327902],"genre_scores_gemma":[0.997406,0.000002531157,0.0006759014,0.0006877189,0.00001861144,0.0001020484,0.00001481588,0.0000224039,0.001069959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3642228,"threshold_uncertainty_score":0.9996278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719082869635042,"score_gpt":0.2200229771198132,"score_spread":0.2028321484234628,"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."}}