{"id":"W2041304098","doi":"10.1111/j.0006-341x.2004.00157.x","title":"Loglinear Models for the Robust Design in Mark–Recapture Experiments","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Log-linear model; Mark and recapture; Statistics; Sampling (signal processing); Population; Econometrics; Sampling design; Poisson regression; Poisson distribution; Mathematics; Linear model; Computer science; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0746392,0.003731727,0.003392534,0.001682498,0.001031231,0.003460326,0.006562511,0.004536859,0.01826722],"category_scores_gemma":[0.1053933,0.001928894,0.003761082,0.002799424,0.003905257,0.004855712,0.003348908,0.00680504,0.004344791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004710205,"about_ca_system_score_gemma":0.003611668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003475987,"about_ca_topic_score_gemma":0.002887333,"domain_scores_codex":[0.9489685,0.03856594,0.001684347,0.005499157,0.004111528,0.0011705],"domain_scores_gemma":[0.9067532,0.07316615,0.007876587,0.008040056,0.003654151,0.0005099842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001107219,0.0003212391,0.002735076,0.001203781,0.0005677316,0.0002012725,0.0007616248,0.1510057,0.002511322,0.7455896,0.006614041,0.08738144],"study_design_scores_gemma":[0.0005360667,0.0009135514,0.001755041,0.0002025135,0.000227645,0.0001075753,0.00007704316,0.529413,0.001585798,0.444776,0.0202306,0.0001751182],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001220881,0.0002333273,0.9963802,0.0001679155,0.0000877046,0.0004705087,0.000365478,0.0003881837,0.0006858408],"genre_scores_gemma":[0.06548198,0.0008667943,0.9105894,0.0006462142,0.0002617361,0.01447908,0.001179287,0.0002839848,0.006211506],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0746392,"threshold_uncertainty_score":0.3947346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2734284867062761,"score_gpt":0.3700501551488151,"score_spread":0.096621668442539,"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."}}