{"id":"W2346311008","doi":"10.1186/s13062-016-0127-4","title":"Estimation of ribosome profiling performance and reproducibility at various levels of resolution","year":2016,"lang":"en","type":"article","venue":"Biology Direct","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation; Tel Aviv University","keywords":"Ribosome profiling; Biology; Computational biology; Profiling (computer programming); Protocol (science); DECIPHER; Ribosome; Computer science; Translation (biology); Data mining; Bioinformatics; Genetics; Gene; Messenger RNA; RNA","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01273882,0.001242375,0.001456106,0.001874456,0.000891162,0.001754806,0.001131847,0.001310551,0.00136883],"category_scores_gemma":[0.02587679,0.0006640672,0.001380833,0.001996408,0.001378722,0.001096814,0.001495997,0.001430044,0.001256692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007586906,"about_ca_system_score_gemma":0.0004591641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001017026,"about_ca_topic_score_gemma":0.0008705839,"domain_scores_codex":[0.9878588,0.003093816,0.001278223,0.003671427,0.00357514,0.0005226821],"domain_scores_gemma":[0.9749978,0.01311213,0.002428888,0.004281837,0.004832445,0.0003470192],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001191436,0.0002325633,0.05136239,0.001050464,0.0006920007,0.0001609092,0.0007289566,0.009222575,0.8875968,0.0005721312,0.001207592,0.04598221],"study_design_scores_gemma":[0.00002324506,0.0008901499,0.1012324,0.00009754911,0.000493615,0.000348741,0.0002454083,0.02577279,0.8652848,0.0009593345,0.004479862,0.0001721489],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7540067,0.004657652,0.2286462,0.000310151,0.0003077798,0.0003566763,0.005971573,0.002790865,0.00295245],"genre_scores_gemma":[0.885578,0.001328429,0.09750949,0.0003401079,0.00009262349,0.0008988681,0.01155783,0.001133438,0.001561226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9872612,"threshold_uncertainty_score":0.06737012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.020437737905942,"score_gpt":0.258482165861756,"score_spread":0.238044427955814,"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."}}