{"id":"W2918977853","doi":"10.1111/aje.12587","title":"Using the Formozov–Malyshev–Pereleshin formula to convert mammal spoor counts into density estimates for long‐term community‐level monitoring","year":2019,"lang":"en","type":"article","venue":"African Journal of Ecology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Mammal; Range (aeronautics); Term (time); Ecology; Statistics; Allometry; Geography; Mathematics; Biology; Physics","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":[0.002009474,0.0005030233,0.0003961526,0.002216955,0.0002420281,0.0008019899,0.0009078705,0.0003500254,0.003001521],"category_scores_gemma":[0.008790037,0.0002663105,0.0004996274,0.0012233,0.0003955648,0.001545263,0.000873631,0.0008179328,0.000765734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004504688,"about_ca_system_score_gemma":0.0004176859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003037247,"about_ca_topic_score_gemma":0.004263386,"domain_scores_codex":[0.9991332,0.000345568,0.00007862353,0.0001385211,0.0002650477,0.00003910057],"domain_scores_gemma":[0.9979594,0.001127186,0.0003022824,0.0002454278,0.0003306557,0.00003514071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001841601,0.0001112488,0.2036342,0.0003077765,0.0002823485,0.0004199138,0.0003130946,0.05114217,0.01045496,0.03529054,0.006358725,0.6915008],"study_design_scores_gemma":[0.00003263399,0.0002928344,0.2091009,0.0002290301,0.0001129536,0.002470436,0.0003139059,0.7060355,0.01803642,0.03762471,0.02561805,0.0001326432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1773282,0.001023159,0.8119653,0.0004001098,0.0001770867,0.0001353778,0.001014491,0.000934375,0.007021912],"genre_scores_gemma":[0.6073382,0.0006183984,0.3876902,0.0001166735,0.00005266846,0.0002096832,0.0007023519,0.0002138854,0.003057995],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003037247,"threshold_uncertainty_score":0.01062721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04871330538392678,"score_gpt":0.2905223349083862,"score_spread":0.2418090295244594,"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."}}