{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004821462,0.0000893081,0.0001080214,0.0004086386,0.00007044789,0.00002916504,0.000120857,0.00008813713,0.00001295881],"category_scores_gemma":[0.0005246524,0.00006343804,0.00004471021,0.001396631,0.00001535761,0.00008849132,0.00001901487,0.00005498152,0.000005924621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001109722,"about_ca_system_score_gemma":0.00002726965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003239302,"about_ca_topic_score_gemma":0.000006026157,"domain_scores_codex":[0.999304,0.00002162368,0.0002219121,0.0001224819,0.0001785587,0.0001514186],"domain_scores_gemma":[0.9990864,0.0005436778,0.00008460181,0.0001833573,0.00007289074,0.00002905691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001456827,0.0007457965,0.0007066928,0.0001546114,0.00005834587,0.000004649367,0.002297438,0.7550943,0.0004971598,0.2054776,0.008898516,0.02591918],"study_design_scores_gemma":[0.002330039,0.0001033646,0.001519872,0.0000430445,0.00004031987,0.000004751895,0.0001606495,0.7129363,0.001770049,0.2778713,0.002931911,0.0002884135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008304445,0.000482197,0.9898759,0.000326867,0.0001911033,0.0006187162,0.00001399412,0.00003312236,0.0001536566],"genre_scores_gemma":[0.6296679,0.00003973526,0.3698623,0.00009039867,0.00008149581,0.00006254311,0.00001680613,0.00002143897,0.0001574631],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6213634,"threshold_uncertainty_score":0.2586928,"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."}}