{"id":"W2051581953","doi":"10.1145/2064696.2064705","title":"Semantic text mining for lignocellulose research","year":2011,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Génome Québec; Genome Canada","keywords":"Computer science; Semantic Web; Identification (biology); Social Semantic Web; World Wide Web; Semantic analytics; Ontology; Semantic Web Stack; Interface (matter); Semantics (computer science); Data science; Information retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005357204,0.00007838001,0.00008879681,0.0000473402,0.000101105,0.00001102262,0.000204779,0.0001668057,0.00008730405],"category_scores_gemma":[0.0003691692,0.0000616251,0.0000575684,0.00008003954,0.0001923461,8.496581e-7,0.000107262,0.00006406254,0.00003358003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003992397,"about_ca_system_score_gemma":0.00004568138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002503771,"about_ca_topic_score_gemma":0.00002514617,"domain_scores_codex":[0.9991361,0.00004218179,0.0001227383,0.0002692855,0.0001053664,0.0003242682],"domain_scores_gemma":[0.999508,0.00005130469,0.00001998639,0.0002412146,0.0001015273,0.00007802944],"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":[0.0004155138,0.000352715,0.006814888,0.0001219763,0.0001419095,0.00001543002,0.00127356,3.267546e-7,0.5464626,0.002298068,0.2343547,0.2077484],"study_design_scores_gemma":[0.0008893833,0.00182899,0.002680043,0.00002951708,0.00001601805,0.00001785367,0.002202026,0.0001555164,0.6803056,0.001632116,0.3099301,0.0003127829],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9360368,0.0009226218,0.01295085,0.0003430803,0.0002219106,0.0002694559,0.000006175979,0.0000533568,0.04919578],"genre_scores_gemma":[0.9634444,0.00004342828,0.02672658,0.0001541598,0.0001772791,0.00004193182,0.0000163242,0.00001444949,0.009381426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2074356,"threshold_uncertainty_score":0.2512999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1777350674694664,"score_gpt":0.3699209237205864,"score_spread":0.19218585625112,"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."}}