{"id":"W6930988409","doi":"10.5281/zenodo.15757714","title":"Inferring Exon and Intron Metadata from .gff file","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Listeria monocytogenes in Food Safety","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Intron; Exon; Pipeline (software); RNA splicing; Metadata; Code (set theory); Source code","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002561849,0.003448194,0.001169062,0.006455958,0.001260933,0.003305024,0.002631556,0.001956092,0.08994511],"category_scores_gemma":[0.009369194,0.001548,0.002195931,0.003777803,0.000656691,0.003079426,0.002632637,0.001661065,0.09387855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001345843,"about_ca_system_score_gemma":0.001881424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006537468,"about_ca_topic_score_gemma":0.005361775,"domain_scores_codex":[0.9986594,0.0001321417,0.0001117688,0.0005114059,0.0004349388,0.0001504213],"domain_scores_gemma":[0.9968342,0.001182368,0.000213155,0.000921273,0.0007065535,0.0001423833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009572905,0.0001547087,0.005550717,0.001511512,0.0001693797,0.0009642017,0.0003330394,0.004389886,0.01584901,0.009406923,0.8239037,0.1368096],"study_design_scores_gemma":[0.0003594361,0.0001170909,0.006952929,0.0005514537,0.0001472126,0.001370564,0.0003659349,0.04730664,0.06723889,0.02782439,0.8474293,0.0003361718],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.004733128,0.0002603973,0.1256446,0.0004546423,0.0002217597,0.0001973965,0.5004227,0.357492,0.01057337],"genre_scores_gemma":[0.01335449,0.0002441489,0.1097417,0.0001852494,0.00005667175,0.0003062378,0.8256316,0.04278544,0.007694513],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.08994511,"threshold_uncertainty_score":0.3008963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0523637425095651,"score_gpt":0.2808535688578397,"score_spread":0.2284898263482746,"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."}}