{"id":"W4367849368","doi":"10.32920/22734329.v1","title":"Towards Alzheimer's Disease Classification through Transfer Learning","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transfer of learning; Artificial intelligence; Deep learning; Computer science; Neuroimaging; Benchmark (surveying); Machine learning; Deep neural networks; Training set; Artificial neural network; Entropy (arrow of time); Pattern recognition (psychology); Psychology; Neuroscience; Cartography","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002551919,0.0003344706,0.0002496865,0.0001484085,0.0003082643,0.0002424313,0.0004958081,0.0002546483,0.0005066608],"category_scores_gemma":[0.0005461401,0.0003234382,0.0002509653,0.0004033752,0.0001555769,0.0001901005,0.0001905049,0.001080926,0.001378926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009091094,"about_ca_system_score_gemma":0.0002352901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006143325,"about_ca_topic_score_gemma":0.00001006883,"domain_scores_codex":[0.9970286,0.0003771497,0.0004464031,0.00123479,0.0005794461,0.0003335629],"domain_scores_gemma":[0.9987581,0.000157058,0.0001178402,0.0007061122,0.00007043732,0.0001904995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006462983,0.0009931024,0.001160493,0.0007267545,0.0002095469,0.0001664826,0.004774628,0.01546896,0.3103833,0.4947824,0.01532664,0.1553614],"study_design_scores_gemma":[0.001578852,0.0001893375,0.1397534,0.0003855058,0.0008066755,0.00002493591,0.001592335,0.2173433,0.3643029,0.09572951,0.1747577,0.003535533],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1944409,0.0006441472,0.418254,0.08873264,0.01759239,0.006614874,0.0003825124,0.02054564,0.2527928],"genre_scores_gemma":[0.9911526,0.0004227126,0.0001603542,0.0009239911,0.0002505373,0.0003823768,0.00008711729,0.00009254769,0.006527743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7967117,"threshold_uncertainty_score":0.9999217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1945249201871302,"score_gpt":0.340440091460728,"score_spread":0.1459151712735977,"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."}}