{"id":"W1998843387","doi":"10.1142/s0129054108006157","title":"COMPUTATION BY ANNOTATION: MODELLING EPIGENETIC REGULATION","year":2008,"lang":"en","type":"article","venue":"International Journal of Foundations of Computer Science","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Modulo; Annotation; Substring; Set (abstract data type); Epigenetics; Theoretical computer science; Computation; Artificial intelligence; Programming language; Biology; Gene; Mathematics; Genetics; Discrete mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002560026,0.00006340627,0.00008172359,0.0001601949,0.0001034881,0.0000433053,0.0004452616,0.00002796236,0.00000489138],"category_scores_gemma":[0.00002580348,0.00006494155,0.00005842073,0.0001442484,0.0002208952,0.00004020476,0.00006548579,0.00004192779,0.000001543684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003693966,"about_ca_system_score_gemma":0.0002594594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004485321,"about_ca_topic_score_gemma":8.216173e-7,"domain_scores_codex":[0.9989517,0.00001783468,0.0003939938,0.0001234662,0.0004345307,0.00007851871],"domain_scores_gemma":[0.997991,0.00001606264,0.000420339,0.00009989448,0.001428436,0.00004422969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002823175,0.0001062819,0.001027569,0.000003258454,0.00004838316,0.000001957774,0.0002355354,0.8048155,0.1820251,0.0005364132,0.0007338237,0.01043799],"study_design_scores_gemma":[0.000752849,0.0003638476,0.01038599,0.0000345182,0.00001193423,0.0003609889,0.00002325467,0.926986,0.05688994,0.002243266,0.001778498,0.0001688559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4636497,0.00004292419,0.5357627,0.0001090021,0.0003494345,0.00002275608,0.00000353332,0.000001215594,0.00005869037],"genre_scores_gemma":[0.8969477,0.00008659231,0.1026837,0.00004176367,0.0001765067,5.223168e-7,0.00003462721,0.000004377655,0.00002423086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.433298,"threshold_uncertainty_score":0.264824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411744160646058,"score_gpt":0.2674833512312262,"score_spread":0.2533659096247657,"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."}}