{"id":"W4327519997","doi":"10.2139/ssrn.4381439","title":"ADSP: An Adaptive Sample Pooling Strategy for Diagnostic Testing","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; University of Victoria","funders":"","keywords":"Pooling; Sample (material); Econometrics; Computer science; Business; Artificial intelligence; Economics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01256146,0.0002301316,0.0005160357,0.0001471264,0.0003517284,0.00009812474,0.000397315,0.0001506697,0.00004115302],"category_scores_gemma":[0.3400448,0.0002010913,0.0001741213,0.0005361,0.0000734252,0.0001564033,0.00005732406,0.001650605,0.00003179758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003860005,"about_ca_system_score_gemma":0.001413582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003737675,"about_ca_topic_score_gemma":0.0001500103,"domain_scores_codex":[0.9950724,0.0005929258,0.000883126,0.0003593073,0.0003648752,0.002727388],"domain_scores_gemma":[0.808487,0.1902998,0.0004061626,0.0002741524,0.0003102387,0.0002225961],"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":[0.0001474745,0.000104888,0.0002785582,0.00002993297,0.0001669512,0.00001030359,0.0000615135,0.000298817,0.0001135909,0.8521468,0.0002034871,0.1464377],"study_design_scores_gemma":[0.0009564438,0.002000334,0.0002298826,0.00007647661,0.0001434243,0.000072626,0.001124183,0.007167562,0.0000674905,0.9878694,0.00005299823,0.0002391432],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04587552,0.0001598506,0.9520957,0.0001957376,0.0004711271,0.0006123296,0.00009441799,0.0002584702,0.0002368056],"genre_scores_gemma":[0.3655344,0.0003368684,0.6318122,0.00006902372,0.001833581,0.00008030295,0.000005857705,0.0001097623,0.0002180871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3274833,"threshold_uncertainty_score":0.8200263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6191996054516808,"score_gpt":0.5531897188962673,"score_spread":0.06600988655541351,"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."}}