{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003437114,0.0005752385,0.0005074557,0.001378209,0.0002526185,0.0008057826,0.0005490899,0.0002777832,0.0008864212],"category_scores_gemma":[0.00855412,0.0002882556,0.0005715044,0.001146614,0.0002229516,0.000929838,0.0004806487,0.0004151881,0.0003027979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002977767,"about_ca_system_score_gemma":0.0002505295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008971487,"about_ca_topic_score_gemma":0.04095631,"domain_scores_codex":[0.9987851,0.0004782976,0.0001752472,0.0002814258,0.0002250354,0.00005486173],"domain_scores_gemma":[0.9937926,0.0027041,0.001424272,0.0005591394,0.001370898,0.0001490383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001436048,0.0000320877,0.9758208,0.00003743427,0.0001909979,0.00003133432,0.0002008224,0.002246042,0.001785544,0.0001126196,0.0001648994,0.01923386],"study_design_scores_gemma":[0.00001280409,0.0003298459,0.9571058,0.00002382565,0.0001488261,0.0001862547,0.000444764,0.03875176,0.002237186,0.0001184675,0.0006118956,0.00002858437],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795368,0.0003266657,0.01758142,0.00003227542,0.00001935706,0.00005792603,0.0005556511,0.0002177352,0.001672171],"genre_scores_gemma":[0.980733,0.0001113167,0.01780288,0.00001721521,0.0000147933,0.00005341629,0.0006274448,0.00004910049,0.0005907267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008971487,"threshold_uncertainty_score":0.01817745,"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."}}