{"id":"W2625186559","doi":"","title":"Assessing Generalization in Connectionist and Rule-Based Models Under the Learning Constraint","year":2001,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Connectionism; Generalization; Constraint (computer-aided design); Lisp; Artificial intelligence; Computer science; Row; Theoretical computer science; Cognitive science; Natural language processing; Mathematics; Artificial neural network; Psychology; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002725744,0.0002102306,0.000181226,0.0001343865,0.0003404473,0.001882793,0.0001516449,0.0001237509,0.0005884388],"category_scores_gemma":[0.00006617338,0.000168308,0.00005978076,0.0003689559,0.0001550111,0.001523198,0.0000708881,0.0005645865,0.0002346612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003788586,"about_ca_system_score_gemma":0.00008514584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001138765,"about_ca_topic_score_gemma":0.000005040959,"domain_scores_codex":[0.9984953,0.0002151812,0.0003420122,0.0004148042,0.0001820337,0.0003506888],"domain_scores_gemma":[0.9993766,0.0002130727,0.0001123822,0.0001634555,0.00002092546,0.0001135809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001712017,0.0003122085,0.894085,0.00002305968,0.00006045351,0.00009623179,0.0002648535,0.01073291,0.0001699719,0.06861929,0.0004638848,0.0250009],"study_design_scores_gemma":[0.003153949,0.0001687823,0.7931859,0.0002965278,0.00002975926,0.0001919841,0.002426462,0.01399303,0.0001441237,0.03060514,0.1547441,0.001060275],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9311017,0.0002588539,0.005311532,0.002328794,0.0001163365,0.0001843176,0.00003264215,0.0002239249,0.0604419],"genre_scores_gemma":[0.9967766,0.00001168113,0.0002698379,0.001449231,0.00009112698,0.00001852876,0.0002352448,0.00004835838,0.001099338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1542802,"threshold_uncertainty_score":0.9991534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03279025880701292,"score_gpt":0.2667644489523872,"score_spread":0.2339741901453742,"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."}}