{"id":"W4407829725","doi":"10.1002/ece3.71020","title":"Qiviut Trace and Macro Element Profile Reflects Muskox Population Trends","year":2025,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Animal Ecology and Behavior Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Cegep de Saint Hyacinthe; Government of Nunavut; Université Laval; Université de Montréal; Yukon Department of Environment; Parks Canada; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Hartmann Fonden; Carlsbergfondet; Aarhus Universitet; Polar Knowledge Canada; Parks Canada; Ministère des Forêts, de la Faune et des Parcs; Agence Nationale de la Recherche","keywords":"Macro; Trace element; TRACE (psycholinguistics); Element (criminal law); Population; Geography; Archaeology; Computer science; Geology; Demography; Sociology; Geochemistry; Political science; Philosophy; Linguistics; Law","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":[0.0002763204,0.0001867425,0.0001417609,0.0008728054,0.0002810476,0.0004181449,0.0001515727,0.0001878162,0.001126359],"category_scores_gemma":[0.0004341988,0.0001187207,0.0001039341,0.0005238995,0.0001794593,0.0002319331,0.0002498721,0.0001496459,0.0001681787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002314363,"about_ca_system_score_gemma":0.0001002159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01012411,"about_ca_topic_score_gemma":0.02887756,"domain_scores_codex":[0.9998969,0.00001661248,0.000007853763,0.0000422485,0.00002102456,0.00001538183],"domain_scores_gemma":[0.9997036,0.00003636576,0.0001281981,0.00001824033,0.00008201507,0.00003153077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007719434,0.00001095264,0.9810181,0.0000142099,0.00003129409,0.00001833951,0.0002718988,0.0001565645,0.01436865,0.00003418367,0.00005299386,0.003945592],"study_design_scores_gemma":[4.73668e-7,0.00004336917,0.9985423,0.000003399583,0.000006410883,0.00004609071,0.0002370955,0.0003211688,0.0006131647,0.00002406151,0.0001610431,0.000001387089],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990362,0.00005882009,0.000323192,0.000007551457,9.924627e-7,0.000003244711,0.0002303418,0.000005831269,0.0003337891],"genre_scores_gemma":[0.9985564,0.00004194957,0.000578299,0.00001248077,0.000001382997,0.000007340845,0.0003566511,0.000003425645,0.0004419958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01012411,"threshold_uncertainty_score":0.0201304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008155271902121265,"score_gpt":0.2672941215179875,"score_spread":0.2591388496158662,"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."}}