{"id":"W2115427074","doi":"10.1186/1471-2105-14-186","title":"MITE Digger, an efficient and accurate algorithm for genome wide discovery of miniature inverted repeat transposable elements","year":2013,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Chromosomal and Genetic Variations","field":"Agricultural and Biological Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Electric (Canada); University of Toronto","funders":"University of Toronto","keywords":"Transposable element; Mite; Genome; Inverted repeat; Computational biology; Computer science; Algorithm; Biology; Genetics; Gene; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000965393,0.0001214243,0.0001590859,0.00001286932,0.0001357584,0.00009577033,0.0001404806,0.00007857887,0.00005152066],"category_scores_gemma":[0.00002328996,0.0000489477,0.00006515814,0.0001514396,0.00004619816,0.0003167095,0.00003353821,0.00004123001,0.000007623588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009338314,"about_ca_system_score_gemma":0.00001212143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004171145,"about_ca_topic_score_gemma":0.0001344363,"domain_scores_codex":[0.9991372,0.00001548353,0.0003859316,0.0001214064,0.0001314291,0.0002085661],"domain_scores_gemma":[0.9994989,0.0001119814,0.0001504175,0.00007133836,0.00008160392,0.00008577848],"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":[0.0002135742,0.002355509,0.09558422,0.001189544,0.0004091074,0.00000180415,0.01146697,0.002420568,0.1514605,0.001096753,0.001997994,0.7318035],"study_design_scores_gemma":[0.0007108616,0.0005582048,0.1759889,0.00003628711,0.00005112078,0.000004422336,0.001120169,0.8159245,0.002155682,0.000425374,0.002720871,0.0003035423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926364,0.00007666964,0.00564293,0.0001479185,0.00007125885,0.0007119133,0.0005750651,0.00002499238,0.00011283],"genre_scores_gemma":[0.7508491,0.0001239886,0.2455994,0.0005956138,0.0001805105,0.0001447018,0.00172476,0.000004070624,0.0007777996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.813504,"threshold_uncertainty_score":0.1996029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01733205074527018,"score_gpt":0.215296391470711,"score_spread":0.1979643407254408,"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."}}