{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003188049,0.0007400587,0.0005930276,0.001262504,0.0003243332,0.0008495722,0.001308565,0.0006724457,0.0009749769],"category_scores_gemma":[0.005067532,0.000597283,0.0009843891,0.0009360719,0.0003423233,0.001914196,0.001542087,0.00105938,0.0002790465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006462486,"about_ca_system_score_gemma":0.0009542266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0176078,"about_ca_topic_score_gemma":0.02991674,"domain_scores_codex":[0.9992235,0.0002811943,0.00005514726,0.0002392965,0.0001280103,0.00007282181],"domain_scores_gemma":[0.9976058,0.001153517,0.0004552436,0.0002305888,0.0004239268,0.0001309104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001246431,0.0005112176,0.3384078,0.0001225945,0.0005978393,0.0001582354,0.0002705339,0.544489,0.01183102,0.002479332,0.00112322,0.09988453],"study_design_scores_gemma":[0.000006452259,0.00007896029,0.01998441,0.00001546936,0.00005782155,0.00004534541,0.00005271152,0.9768994,0.001014951,0.00129027,0.0005325557,0.00002166634],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4018354,0.0004201957,0.5943133,0.000438203,0.00006275414,0.00006837992,0.0005560828,0.0009921897,0.001313516],"genre_scores_gemma":[0.8118637,0.000132339,0.1862541,0.0001426996,0.00004268724,0.0000780827,0.0006626142,0.00006764491,0.0007560962],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0176078,"threshold_uncertainty_score":0.03501064,"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."}}