{"id":"W2160118913","doi":"10.1371/journal.pone.0076093","title":"Semantic Annotation of Mutable Data","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Harvard University; National Science Foundation","keywords":"Annotation; Computer science; Metadata; Information retrieval; Temporal annotation; Ontology; Metadata repository; Data quality; Data curation; Terminology; World Wide Web; Data science; Natural language processing; Artificial intelligence; Service (business); Natural 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":[0.02174385,0.0008997297,0.001200578,0.006572265,0.003746241,0.008322509,0.004148894,0.002600383,0.004854606],"category_scores_gemma":[0.0355385,0.000941163,0.002391427,0.006183878,0.008589826,0.02077072,0.01026155,0.003428525,0.001648735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003807938,"about_ca_system_score_gemma":0.004214665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005014979,"about_ca_topic_score_gemma":0.003854739,"domain_scores_codex":[0.9799905,0.006766981,0.002533268,0.003388925,0.006520048,0.0008002732],"domain_scores_gemma":[0.949504,0.01703398,0.002863336,0.02353389,0.006251107,0.0008135684],"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":[0.000121414,0.00006922295,0.002042626,0.0004361714,0.00007715708,0.0006957159,0.005201438,0.004340775,0.004586266,0.9184311,0.007048177,0.05694999],"study_design_scores_gemma":[0.00002963006,0.00003869655,0.0008898407,0.0005218572,0.0001203334,0.0005557202,0.0012571,0.0289504,0.02026249,0.5886503,0.3586195,0.0001043112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008552346,0.0003375209,0.9682811,0.002592828,0.0003933408,0.0002654923,0.001450038,0.003401616,0.01472575],"genre_scores_gemma":[0.2013986,0.001037665,0.770034,0.001572919,0.0004537573,0.0006898153,0.005855394,0.002308691,0.0166492],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02174385,"threshold_uncertainty_score":0.1149938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4603301666673051,"score_gpt":0.3722381261169688,"score_spread":0.08809204055033637,"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."}}