{"id":"W4285140022","doi":"10.1007/978-3-031-06220-9_8","title":"Reconciliation with Segmental Duplication, Transfer, Loss and Gain","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Gene duplication; Tree (set theory); CRISPR; Genome; Set (abstract data type); Gene; Computer science; Dynamic programming; Diversification (marketing strategy); Segmental duplication; Theoretical computer science; Gene family; Biology; Computational biology; Algorithm; Combinatorics; Genetics; Mathematics; Programming language","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":[0.0031369,0.0007662975,0.001429366,0.001082525,0.001131228,0.003374638,0.002363773,0.001928949,0.009822187],"category_scores_gemma":[0.009698601,0.0006243444,0.0012563,0.002386549,0.004048766,0.00602415,0.004214377,0.002714276,0.003288952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008852016,"about_ca_system_score_gemma":0.0009130704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003695894,"about_ca_topic_score_gemma":0.0005445575,"domain_scores_codex":[0.9981358,0.0007369992,0.0001119668,0.0004806514,0.0003486504,0.0001859123],"domain_scores_gemma":[0.9966665,0.001834951,0.0001563652,0.00114711,0.0001405124,0.00005457188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003980619,0.00004053403,0.0006802056,0.0005298791,0.0001230917,0.0008478329,0.0008080616,0.0234322,0.008282244,0.6645778,0.01593158,0.2843484],"study_design_scores_gemma":[0.00003850902,0.00004234603,0.0002162922,0.00007762532,0.00006192555,0.0007875816,0.000153398,0.01771641,0.007486307,0.9178542,0.05552927,0.0000360435],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04547929,0.01207068,0.8396774,0.004143168,0.002479124,0.0001416905,0.000831267,0.004234768,0.09094263],"genre_scores_gemma":[0.5909765,0.00869372,0.3459616,0.002009124,0.0009901962,0.000371952,0.002464896,0.003385191,0.04514682],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009822187,"threshold_uncertainty_score":0.03285849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006469011369344214,"score_gpt":0.240688279890445,"score_spread":0.2342192685211008,"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."}}