{"id":"W2058570609","doi":"10.1002/meet.14504901318","title":"Exploring digital information using tags and local knowledge","year":2012,"lang":"en","type":"article","venue":"Proceedings of the American Society for Information Science and Technology","topic":"Web and Library Services","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Metadata; Subject (documents); World Wide Web; Computer science; Information retrieval; Conjunction (astronomy)","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":[0.003787645,0.0001780106,0.0002421319,0.005339005,0.002056373,0.005803272,0.0006445242,0.0005522596,0.003270617],"category_scores_gemma":[0.0119205,0.0001882876,0.0001862064,0.005767329,0.003030133,0.007872869,0.003534976,0.0005783599,0.0003724166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001835764,"about_ca_system_score_gemma":0.001277403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005694556,"about_ca_topic_score_gemma":0.01047871,"domain_scores_codex":[0.9967976,0.002187998,0.0001503623,0.0001971755,0.0004801682,0.00018675],"domain_scores_gemma":[0.9873717,0.009571786,0.001040792,0.0008086881,0.0008802671,0.0003268477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001386274,0.0001390741,0.08578362,0.0005576797,0.00003933121,0.000868985,0.7898603,0.0003177851,0.004278483,0.01706032,0.001119408,0.09983639],"study_design_scores_gemma":[0.00001524268,0.0001269509,0.03827111,0.0002899579,0.00004950756,0.0004741604,0.8918241,0.001455098,0.003069086,0.007718318,0.05666,0.00004644617],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653745,0.0002855415,0.005308948,0.0006673516,0.00001420236,0.00006079737,0.000208527,0.00005750873,0.02802268],"genre_scores_gemma":[0.996519,0.0001601454,0.001942579,0.0000442275,0.000005925701,0.00002239914,0.00006661857,0.00001117272,0.001227987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005803272,"threshold_uncertainty_score":0.02003127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03128234210205751,"score_gpt":0.2455422407537823,"score_spread":0.2142598986517248,"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."}}