{"id":"W2724387372","doi":"10.32628/ijsrset1732170","title":"A Semantic Metadata Enrichment Software Ecosystem based on Sentiment and Emotion Metadata Enrichments","year":2017,"lang":"en","type":"article","venue":"International Journal of Scientific Research in Science Engineering and Technology","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; École de Technologie Supérieure; Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Metadata; Computer science; World Wide Web; Ecosystem; Information retrieval; Ecology; Biology","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007777783,0.000105978,0.0001915167,0.004014547,0.0004966239,0.003921301,0.003795372,0.00004820616,0.000002063756],"category_scores_gemma":[0.00194132,0.0000867042,0.00002937322,0.0011068,0.0005457416,0.002813592,0.001267326,0.000363282,0.000002910088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001591856,"about_ca_system_score_gemma":0.0002462905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000163429,"about_ca_topic_score_gemma":0.000005283389,"domain_scores_codex":[0.9969729,0.00004547496,0.0003550868,0.0005268386,0.001739268,0.0003604204],"domain_scores_gemma":[0.9979782,0.0001444619,0.0002227267,0.0008473751,0.0006458873,0.0001613109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000876776,0.001558584,0.1096116,0.000215824,0.0006015493,0.002539393,0.001114676,0.02039573,0.1474709,0.1789103,0.001205666,0.536288],"study_design_scores_gemma":[0.001254681,0.0003168071,0.0146942,0.0008381775,0.00001766308,0.0002942985,0.0002132135,0.9596162,0.01414197,0.002625188,0.005687311,0.0003002388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8328573,0.0002055409,0.1567985,0.008128614,0.001782719,0.0001259304,0.00001877701,0.00003860344,0.00004394828],"genre_scores_gemma":[0.9804754,0.0000413551,0.01934268,0.000008380569,0.00002964398,0.000004133898,0.000001794569,0.000003513957,0.00009308544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9392205,"threshold_uncertainty_score":0.9971128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04244368316874624,"score_gpt":0.3446516817234985,"score_spread":0.3022079985547522,"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."}}