{"id":"W2045959057","doi":"10.1890/13-1497.1","title":"Compensatory heterogeneity in spatially explicit capture–recapture data","year":2013,"lang":"en","type":"article","venue":"Ecology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Forests","funders":"","keywords":"Statistics; Mark and recapture; Range (aeronautics); Sampling (signal processing); Scale (ratio); Mathematics; Spatial variability; Detector; Ecology; Biology; Geography; Physics; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002320573,0.0001027523,0.000148675,0.00002812939,0.00006733512,0.000009982922,0.0004987187,0.0001883987,0.009750381],"category_scores_gemma":[0.00006893375,0.0001003974,0.00001636032,0.00009876132,0.0001130588,0.0003081894,0.0004478146,0.000184913,0.004338242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009256771,"about_ca_system_score_gemma":0.00001946404,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001896934,"about_ca_topic_score_gemma":0.04810676,"domain_scores_codex":[0.9989448,0.0001416548,0.0001932517,0.0003722125,0.00007532112,0.0002728225],"domain_scores_gemma":[0.9992707,0.0001023065,0.00006533345,0.000497377,0.00000542912,0.00005885538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004761927,0.00006095151,0.9766814,0.000001290371,0.000004695653,0.000008751301,0.00008824164,0.0001686457,0.0004028877,0.00007248141,0.02192747,0.0005784096],"study_design_scores_gemma":[0.0002456541,0.00002922858,0.9923055,0.000001097022,0.000003600356,0.000008355642,0.00002721181,0.002109179,0.00004640595,0.0005193279,0.004598679,0.0001058009],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922067,0.00001515676,0.00005379665,0.002724811,0.0003026349,0.0002789173,0.000005767351,0.00002836476,0.004383867],"genre_scores_gemma":[0.9932552,0.000004130632,0.000561624,0.005700797,0.00004001457,0.00005735012,0.00005807025,0.000007716101,0.0003151265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04620982,"threshold_uncertainty_score":0.996437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02094707154005993,"score_gpt":0.2362509106192711,"score_spread":0.2153038390792112,"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."}}