{"id":"W4200139099","doi":"10.1109/ictai52525.2021.00111","title":"Incremental Feature Learning Using Constructive Neural Networks","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Constructive; Computer science; Artificial intelligence; Feature (linguistics); Artificial neural network; Machine learning; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000313693,0.0004187204,0.0003601298,0.0002237308,0.0003173118,0.001508743,0.001551748,0.0001794961,0.0006255],"category_scores_gemma":[0.0002698447,0.0004012226,0.0001204509,0.000685204,0.0002766335,0.001481043,0.0004528466,0.0009546407,0.00006551843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002451599,"about_ca_system_score_gemma":0.0003618397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008538554,"about_ca_topic_score_gemma":0.00009355429,"domain_scores_codex":[0.9967763,0.000233815,0.0005193006,0.00105541,0.0008646335,0.0005505373],"domain_scores_gemma":[0.9976179,0.0002266795,0.0003391918,0.000660762,0.000975647,0.0001798392],"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":[0.0002799789,0.0002786512,0.001758689,0.00001027217,0.0002360068,0.0005606341,0.000675026,0.0206801,0.0179627,0.5289582,0.0005927281,0.428007],"study_design_scores_gemma":[0.00008434704,0.0003474776,0.0001090779,0.0002156395,0.0000203213,0.0002360871,0.00127257,0.8790135,0.1122529,0.005581951,0.0003169448,0.0005492422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06907158,0.00003083426,0.9175338,0.001620863,0.00172362,0.0002866381,0.00006285597,0.0002942649,0.009375568],"genre_scores_gemma":[0.9411411,0.00004518566,0.05764609,0.0003758721,0.000390332,0.00003312549,0.0001134106,0.00002835586,0.0002264741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8720695,"threshold_uncertainty_score":0.999844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066708775952343,"score_gpt":0.3279615675665108,"score_spread":0.2212906899712765,"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."}}