{"id":"W218939381","doi":"","title":"York University at TREC 2012: Microblog Track.","year":2012,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Microblogging; Social media; Computer science; Track (disk drive); Task (project management); Information retrieval; Component (thermodynamics); World Wide Web; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006949595,0.003231322,0.001941417,0.004547769,0.002890515,0.00382647,0.002350997,0.001963829,0.09294663],"category_scores_gemma":[0.01122645,0.0008879018,0.0005008347,0.004269154,0.0006743216,0.003871182,0.001437927,0.002671613,0.08078127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003564785,"about_ca_system_score_gemma":0.005611433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1481361,"about_ca_topic_score_gemma":0.2682621,"domain_scores_codex":[0.9965525,0.0007385135,0.0002296541,0.0004685391,0.001621995,0.0003888881],"domain_scores_gemma":[0.9870042,0.001503596,0.0005097398,0.001480488,0.007364985,0.002137029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001263722,0.0001729883,0.0003035677,0.0001838431,0.00001666112,0.00002227411,0.00002372512,0.0003460769,0.001991268,0.0002439517,0.9746904,0.02187877],"study_design_scores_gemma":[0.00100401,0.0007837317,0.02338461,0.000254944,0.0001314933,0.0002398796,0.0002911823,0.01868555,0.0202739,0.00275521,0.9318774,0.0003181713],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0290523,0.009333902,0.022105,0.01430561,0.006357309,0.004117667,0.6694995,0.064375,0.1808536],"genre_scores_gemma":[0.0395139,0.001797808,0.02501613,0.001770261,0.001034916,0.001864544,0.6814609,0.00329124,0.2442504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1481361,"threshold_uncertainty_score":0.3109374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03678406851436043,"score_gpt":0.2599598306261263,"score_spread":0.2231757621117658,"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."}}