{"id":"W4400959673","doi":"10.23952/jano.6.2024.3.03","title":"Unsupervised sample selection for active learning with quadratic programming","year":2024,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Computer science; Machine learning; Sample (material); Artificial intelligence; Active learning (machine learning); Unsupervised learning; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004468912,0.001740706,0.002429373,0.001518562,0.0007453965,0.001501658,0.003612876,0.002019172,0.002850285],"category_scores_gemma":[0.01080823,0.001075333,0.001453283,0.001355718,0.001875996,0.002286955,0.002473708,0.002800024,0.0006730811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001052662,"about_ca_system_score_gemma":0.001430922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002632877,"about_ca_topic_score_gemma":0.003452203,"domain_scores_codex":[0.9979649,0.001004909,0.00008446651,0.0003853704,0.0004398178,0.0001206255],"domain_scores_gemma":[0.9936877,0.004696619,0.000336114,0.0002950402,0.0008080568,0.0001765415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002585408,0.000246167,0.001494013,0.0003474379,0.0002003345,0.0001301284,0.0001806276,0.7208311,0.003564576,0.04011621,0.004728524,0.2279023],"study_design_scores_gemma":[0.000008378616,0.00001635948,0.00003141773,0.00000509211,0.000004806509,0.000008066379,0.000003905239,0.9951768,0.0002684692,0.004199493,0.0002739069,0.000003189294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001940868,0.0001468915,0.9972621,0.00009765527,0.00002099068,0.00003165931,0.00001651775,0.0001706585,0.0003126623],"genre_scores_gemma":[0.3456905,0.0006149625,0.646132,0.0006489903,0.0003362322,0.0009909136,0.000708428,0.0004580313,0.00442005],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004468912,"threshold_uncertainty_score":0.0236342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005992738286746913,"score_gpt":0.2346928471442198,"score_spread":0.2287001088574729,"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."}}