{"id":"W1758661201","doi":"10.1007/978-3-319-24462-4_2","title":"Extended Spearman and Kendall Coefficients for Gene Annotation List Correlation","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Annotation; Correlation; Spearman's rank correlation coefficient; Gene ontology; Gene Annotation; Computer science; Key (lock); Correlation coefficient; Pearson product-moment correlation coefficient; Data mining; Machine learning; Gene; Statistics; Artificial intelligence; Mathematics; Biology; Genetics; Genome","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009480002,0.0003326413,0.0002931909,0.000577351,0.000214104,0.0007417282,0.001503092,0.0001454923,0.000004711882],"category_scores_gemma":[0.00007225974,0.0003188352,0.00005185885,0.0003501819,0.0003112381,0.0009401764,0.0009525357,0.0002498917,0.0000170485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520996,"about_ca_system_score_gemma":0.0001603591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000104753,"about_ca_topic_score_gemma":0.00001657801,"domain_scores_codex":[0.9973446,0.00001808429,0.0003475221,0.001161891,0.0007213359,0.0004065494],"domain_scores_gemma":[0.9984418,0.000158842,0.0002380212,0.0007227727,0.0002969108,0.0001416689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008507035,0.00002913846,0.00002794522,0.00003414197,0.000009083826,0.00001837033,0.0004190429,0.01025714,0.00005020594,0.02076747,0.0002478375,0.9681311],"study_design_scores_gemma":[0.0004618389,0.000173668,0.0001400062,0.00008657313,0.00001098859,0.00001550064,2.444352e-7,0.9207729,0.0001546285,0.0694892,0.00829725,0.0003972398],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005059269,0.0003053703,0.995389,0.0003413821,0.001815118,0.0006152489,0.00002564161,0.0001097127,0.001347916],"genre_scores_gemma":[0.05100997,0.0000578487,0.9447832,0.0008953155,0.0007212675,0.00002022545,0.0002069642,0.00004745141,0.002257805],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9677339,"threshold_uncertainty_score":0.9999264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02809335195549204,"score_gpt":0.2689284005309168,"score_spread":0.2408350485754247,"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."}}