{"id":"W7039176316","doi":"","title":"Learning and Aligning Structured Random Feature Networks","year":2024,"lang":"en","type":"article","venue":"Western CEDAR (Western Washington University)","topic":"Ichthyology and Marine Biology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Institutes of Health; National Science Foundation","keywords":"Embedding; Feature (linguistics); Key (lock); Artificial neural network; Code (set theory); Convolutional neural network; Function (biology); Feature learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001372069,0.0007095571,0.000792285,0.0005241935,0.0002941329,0.0007224401,0.001264589,0.001201973,0.001501711],"category_scores_gemma":[0.007064585,0.0006184664,0.0005574719,0.0004926195,0.0008863785,0.001622283,0.001153648,0.001228839,0.0005369475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000702154,"about_ca_system_score_gemma":0.0004990915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001604531,"about_ca_topic_score_gemma":0.002267652,"domain_scores_codex":[0.9993586,0.0002452788,0.00002697744,0.0001996809,0.00009892786,0.00007070344],"domain_scores_gemma":[0.9981976,0.001042727,0.0002446861,0.0002195055,0.000240432,0.00005505272],"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":[0.00004729089,0.0000416896,0.001056229,0.00002501188,0.00002573278,0.00005012418,0.0000507396,0.9181712,0.002239418,0.01337213,0.0006198307,0.06430049],"study_design_scores_gemma":[0.000002908414,0.0000110606,0.00005970145,0.000001944199,0.000001519994,0.000005576886,0.000002874749,0.9933931,0.000304553,0.006095704,0.0001189228,0.00000202884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0875034,0.0001389669,0.9085282,0.0002289594,0.0000335329,0.00003933546,0.00006213315,0.000677067,0.002788334],"genre_scores_gemma":[0.8009426,0.0001009994,0.195251,0.0001891198,0.00005982414,0.0001072941,0.0002871066,0.0001351125,0.002926943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001604531,"threshold_uncertainty_score":0.007256269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005194823379191044,"score_gpt":0.2006762261966234,"score_spread":0.1954814028174323,"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."}}