{"id":"W2179470381","doi":"10.2193/0022-541x(2005)069[1053:pcictm]2.0.co;2","title":"PELLET COUNT INDICES COMPARED TO MARK–RECAPTURE ESTIMATES FOR EVALUATING SNOWSHOE HARE DENSITY","year":2005,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Forest Service; National Science Foundation","keywords":"Pellet; Regression; Statistics; Mark and recapture; Regression analysis; Sampling (signal processing); Environmental science; Snowshoe hare; Linear regression; Mathematics; Geography; Ecology; Biology; Predation; Demography; Population; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001223773,0.0001590163,0.0002536586,0.00009544967,0.0002228039,0.00005717127,0.0003512276,0.00006148058,0.0008731957],"category_scores_gemma":[0.0001230112,0.0001403533,0.00009946548,0.0001653527,0.0000487252,0.0003140171,0.0001834645,0.0001407528,0.0001935745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000300066,"about_ca_system_score_gemma":0.00001562448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001521052,"about_ca_topic_score_gemma":0.0001244673,"domain_scores_codex":[0.9984758,0.00005739062,0.0004956248,0.0002117129,0.0004858715,0.0002736378],"domain_scores_gemma":[0.9990841,0.0001364936,0.0004182997,0.0001693961,0.00005635078,0.0001353496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002948472,0.0001869278,0.4187376,0.0000439638,0.0001556499,0.00001798538,0.0006668069,0.04407935,0.00008978484,0.0000966068,0.5212795,0.01435095],"study_design_scores_gemma":[0.001152945,0.0002793546,0.8288045,0.00009197446,0.0002031064,0.00002664115,0.0005290245,0.01162557,0.00008511155,0.0004589088,0.1565167,0.0002261817],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688489,0.00005541857,0.006830892,0.02089119,0.0003454097,0.0006426166,0.000006919224,0.00002013211,0.002358524],"genre_scores_gemma":[0.897458,0.00001808517,0.08197581,0.01900872,0.0002907925,0.00003308006,0.000008393755,0.00001784185,0.001189304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4100669,"threshold_uncertainty_score":0.9560881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02354429275403781,"score_gpt":0.2830811297807218,"score_spread":0.259536837026684,"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."}}