{"id":"W3003623037","doi":"10.1002/ece3.6052","title":"Complete tag loss in capture–recapture studies affects abundance estimates: An elephant seal case study","year":2020,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Census and Population Estimation","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mark and recapture; Abundance (ecology); Abundance estimation; Biology; Seal (emblem); Ecology; Geography; Fishery; Zoology; Demography; Population; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003041383,0.0001487472,0.0002987366,0.00005985146,0.0002002844,0.0000122705,0.00004810683,0.0001092821,0.00001532352],"category_scores_gemma":[0.0003625809,0.0001364318,0.00002109983,0.0001506405,0.00007546528,0.0001865648,0.00004061667,0.0001761148,0.000008327625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009180376,"about_ca_system_score_gemma":0.00002257093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001120288,"about_ca_topic_score_gemma":0.009282546,"domain_scores_codex":[0.9990143,0.0001847634,0.0002400138,0.0002750035,0.00008357816,0.0002023545],"domain_scores_gemma":[0.9993811,0.0002591267,0.0001183304,0.0001128256,0.00006110815,0.00006749057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001310738,0.0004636018,0.9578251,0.0002638386,0.00007818798,0.00121925,0.02891344,0.001490924,0.0001927899,0.007763571,0.001378039,0.0002802084],"study_design_scores_gemma":[0.001893465,0.0008185122,0.901094,0.00003424992,0.000107219,0.0007827554,0.009064533,0.07120914,0.000007273327,0.01460917,0.0000858252,0.0002938946],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967866,0.0003011557,0.0009074084,0.001165672,0.0001731854,0.0005448848,0.00001386622,0.00007623334,0.00003101213],"genre_scores_gemma":[0.9978733,0.000009004269,0.001763163,0.0001800745,0.00009631562,0.0000349884,0.00001976526,0.00001164477,0.00001171786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06971821,"threshold_uncertainty_score":0.5563526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07433035115526256,"score_gpt":0.3513041371842878,"score_spread":0.2769737860290252,"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."}}