{"id":"W2026680199","doi":"10.1111/j.0006-341x.2004.00236.x","title":"Letter to the Editor of <i>Biometrics</i>","year":2004,"lang":"en","type":"letter","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Population; Mathematics; Biometrics; Statistics; Library science; Demography; Computer science; Artificial intelligence; Sociology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01071932,0.001199862,0.003098863,0.00123491,0.003131882,0.005550604,0.002903764,0.0372536,0.007193199],"category_scores_gemma":[0.05176077,0.0009714567,0.001664652,0.001085362,0.002619057,0.002180256,0.0008275865,0.02930762,0.007573571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004414124,"about_ca_system_score_gemma":0.004254007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004773989,"about_ca_topic_score_gemma":0.009389166,"domain_scores_codex":[0.9937348,0.002120039,0.0007611453,0.0008053266,0.002025182,0.0005534995],"domain_scores_gemma":[0.9757652,0.01595461,0.001158575,0.000756541,0.004997232,0.00136778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005471068,0.00001432184,0.0001258248,0.00002980505,0.00001022451,0.0001638003,0.00002145734,0.00003358206,0.00005583413,0.001025903,0.9950125,0.003452034],"study_design_scores_gemma":[0.0001405769,0.00008113597,0.001262851,0.0002596975,0.00006452778,0.0005527157,0.0001846635,0.0008038121,0.0004061355,0.006184944,0.9900061,0.00005273832],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0002621509,0.001197516,0.0006085131,0.8892406,0.1035771,0.00004248019,0.0001952304,0.00007706824,0.004799518],"genre_scores_gemma":[0.002209964,0.0007900926,0.0006887795,0.9046203,0.07584862,0.00009954552,0.00005459271,0.0000525565,0.01563554],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.0372536,"threshold_uncertainty_score":0.05668992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1661029540290433,"score_gpt":0.4268696127936606,"score_spread":0.2607666587646174,"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."}}