{"id":"W2145510128","doi":"10.1586/erm.11.80","title":"TB diagnostics in India: creating an ecosystem for innovation","year":2011,"lang":"en","type":"article","venue":"Expert Review of Molecular Diagnostics","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Division of Chemistry","keywords":"Molecular diagnostics; Medicine; Environmental resource management; Business; Computational biology; Biology; Environmental science; Bioinformatics","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":[0.001227875,0.0001988867,0.000804893,0.0002591636,0.00003385084,0.000004607847,0.0001782557,0.0001570742,0.00007068824],"category_scores_gemma":[0.03343122,0.0001705263,0.000130767,0.0005696651,0.00005137832,0.00007296211,0.00005820462,0.0002088533,0.00001150603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007915594,"about_ca_system_score_gemma":0.0001436984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008717534,"about_ca_topic_score_gemma":0.0000149237,"domain_scores_codex":[0.9977662,0.000257822,0.001004493,0.0003196764,0.0002551918,0.0003966408],"domain_scores_gemma":[0.9974107,0.001169077,0.000263357,0.0004987657,0.0005049492,0.0001531363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006614007,0.007024487,0.4660846,0.07266702,0.0009324173,0.002028781,0.004747512,0.00001482748,0.03001418,0.1267536,0.09695586,0.1921153],"study_design_scores_gemma":[0.01247948,0.01429561,0.3897361,0.1650929,0.00108405,0.0004518861,0.001425818,0.003279979,0.2074182,0.00554406,0.196368,0.002823869],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.4539977,0.5083468,0.01834032,0.002982476,0.0005696797,0.007914687,0.0001633732,0.0001169753,0.007567972],"genre_scores_gemma":[0.4733211,0.4698707,0.0415203,0.01332935,0.0002697593,0.00103908,0.0005563539,0.00008246844,0.00001094949],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.1892915,"threshold_uncertainty_score":0.9747106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05088060495477043,"score_gpt":0.3843377838934796,"score_spread":0.3334571789387091,"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."}}