{"id":"W4213393511","doi":"10.21775/cimb.011.0i1","title":"A High-Resolution Melting Approach for Analyzing Allelic Expression Dynamics","year":2009,"lang":"en","type":"article","venue":"Current Issues in Molecular Biology","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Dynamics (music); Expression (computer science); Allele; Statistical physics; Computer science; Evolutionary biology; Computational biology; Biology; Genetics; Physics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008981251,0.000320084,0.0004586785,0.0008135766,0.0004420455,0.0005411797,0.0006051697,0.0005845463,0.001308337],"category_scores_gemma":[0.001713735,0.0004325467,0.0005060273,0.0007435821,0.0003465255,0.0003900234,0.000415662,0.001686988,0.001292842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002909179,"about_ca_system_score_gemma":0.0001983381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005880021,"about_ca_topic_score_gemma":0.001562096,"domain_scores_codex":[0.9993033,0.0001462947,0.00003479019,0.000290903,0.0001628724,0.00006177206],"domain_scores_gemma":[0.9988847,0.0004781944,0.0002016684,0.0002474411,0.0001313967,0.0000566145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006905312,0.00002462472,0.001551328,0.00006731995,0.00003249531,0.00003386962,0.0001209594,0.0003559905,0.9810686,0.0004830828,0.0001526023,0.01604016],"study_design_scores_gemma":[0.00001446989,0.0002490378,0.02640499,0.00002800104,0.00012538,0.0008443064,0.0002103051,0.02749083,0.9332908,0.001017681,0.01024435,0.00007982552],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3761526,0.001420869,0.6131446,0.0002577816,0.000134201,0.0001605743,0.002086722,0.002094061,0.00454856],"genre_scores_gemma":[0.5731638,0.0005888459,0.4199593,0.0002510117,0.0000393167,0.0003547217,0.001568494,0.0004979455,0.00357668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001308337,"threshold_uncertainty_score":0.004749775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03184094599464012,"score_gpt":0.3209723198426345,"score_spread":0.2891313738479944,"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."}}