{"id":"W3163166489","doi":"10.1002/ecs2.3443","title":"Integrating counts, telemetry, and non‐invasive DNA data to improve demographic monitoring of an endangered species","year":2021,"lang":"en","type":"article","venue":"Ecosphere","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Trent University; Parks Canada","funders":"University of Montana","keywords":"Woodland caribou; Vital rates; Endangered species; Population; Mark and recapture; Biology; Abundance (ecology); Population size; Juvenile; Ecology; Geography; Demography; Habitat; Population growth","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001074421,0.00005606085,0.00007708093,0.000006661262,0.00005895309,0.0000149441,0.0001432801,0.00003995358,0.001379702],"category_scores_gemma":[0.0001144187,0.00005548183,0.00001026384,0.0001476034,0.00004187463,0.0001755318,0.0001897716,0.00006126383,0.00008712301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001925683,"about_ca_system_score_gemma":0.00001657226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008417095,"about_ca_topic_score_gemma":0.001626466,"domain_scores_codex":[0.999508,0.00002050589,0.0001001154,0.0002052128,0.00007241886,0.00009376925],"domain_scores_gemma":[0.9996039,0.00004901573,0.00004049358,0.0002546048,0.00001031522,0.00004162237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000428283,0.00002570507,0.9439544,0.000009478531,0.00001240486,0.000007517519,0.0001931931,0.000005059278,0.03126647,0.00002236199,0.0100884,0.01441068],"study_design_scores_gemma":[0.0000804598,0.00003790138,0.9829283,0.00001542106,0.000008594951,0.000002741016,0.0008098737,0.0001773663,0.01337555,0.00008829992,0.002405742,0.00006972168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900988,0.00005948359,0.00005876863,0.0001307375,0.0001799333,0.00006048777,0.00002442136,0.000009303881,0.009378101],"genre_scores_gemma":[0.9916106,0.00003226379,0.007460299,0.00009858747,0.00006644626,0.000003896932,0.00001951649,0.000005470883,0.0007028715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03897389,"threshold_uncertainty_score":0.9995332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047588501231863,"score_gpt":0.2394110417927807,"score_spread":0.2189351567804621,"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."}}