{"id":"W4283791154","doi":"10.1609/aaai.v36i11.21593","title":"Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"MNIST database; Computer science; Artificial intelligence; Annotation; Deep learning; Active learning (machine learning); Machine learning; Context (archaeology); Task (project management); Labeled data; Biology; 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":[0.005173429,0.000951396,0.00132571,0.0007247125,0.0007061334,0.002285168,0.003086002,0.002701775,0.006080792],"category_scores_gemma":[0.01439533,0.0006633748,0.0009471451,0.0008737618,0.001471784,0.003087666,0.003028119,0.003827806,0.001737602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001201535,"about_ca_system_score_gemma":0.00103174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003814884,"about_ca_topic_score_gemma":0.005693679,"domain_scores_codex":[0.9980262,0.000825918,0.00008526545,0.0005457178,0.0003487036,0.0001680839],"domain_scores_gemma":[0.990045,0.006286006,0.0003687422,0.001654075,0.001247733,0.0003983999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00111547,0.0005046806,0.002573775,0.0003069064,0.0001463172,0.000310202,0.0002863398,0.2307518,0.01729036,0.03208127,0.03772584,0.6769072],"study_design_scores_gemma":[0.00003199713,0.00006087208,0.0002851982,0.00002239797,0.00001315983,0.00005035402,0.00002023289,0.967508,0.008246022,0.02105758,0.002688774,0.00001543259],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02881184,0.0008470501,0.9628192,0.002018366,0.0003849385,0.00008202538,0.0004846074,0.001870921,0.002681197],"genre_scores_gemma":[0.5762984,0.0006370395,0.3939891,0.00130104,0.0008035922,0.000358367,0.00273788,0.0007414428,0.0231332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006080792,"threshold_uncertainty_score":0.02736002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07314071175042759,"score_gpt":0.3163381213504547,"score_spread":0.2431974096000272,"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."}}