{"id":"W4385712138","doi":"10.1007/978-3-031-39141-5_17","title":"Scoring Ontologies for Reuse: An Approach for Fitting Semantic Requirements","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Ontology; Reuse; Set (abstract data type); Process (computing); Selection (genetic algorithm); Information retrieval; Software engineering; Data science; Artificial intelligence; Programming language; Engineering","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.002015454,0.0002215445,0.0003109781,0.0007818258,0.000778345,0.0008203021,0.005774509,0.0001350333,3.417475e-7],"category_scores_gemma":[0.0003703651,0.000214828,0.00006359682,0.0003033806,0.0005159699,0.006643733,0.002974219,0.000199287,0.00000592975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009722377,"about_ca_system_score_gemma":0.0002095004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001241862,"about_ca_topic_score_gemma":0.00001165547,"domain_scores_codex":[0.9981946,0.0000211714,0.0007069376,0.0004092544,0.0003176451,0.0003504428],"domain_scores_gemma":[0.995667,0.0005750814,0.0003780026,0.002879903,0.000429752,0.00007023893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004151501,0.00001711457,0.00006821522,0.000156844,0.000009591144,1.597269e-7,0.002322944,0.0004805915,0.000004062186,0.8008379,0.0002654001,0.195833],"study_design_scores_gemma":[0.0003806792,0.0001133727,0.0007418909,0.0001970141,0.000009098486,0.000009773109,0.0001246886,0.9460357,0.00001465781,0.04093754,0.01110904,0.0003265043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000068663,0.0001514661,0.9749318,0.0006453466,0.0004331775,0.000960527,0.00001315342,0.0002924608,0.02250342],"genre_scores_gemma":[0.012405,0.0007202313,0.9850593,0.0003987974,0.00005635873,0.0002260975,0.0001047677,0.00001386772,0.001015593],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9455552,"threshold_uncertainty_score":0.9996047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1817424823520759,"score_gpt":0.36096108394355,"score_spread":0.1792186015914741,"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."}}