{"id":"W4389665291","doi":"10.1080/17480272.2023.2293177","title":"Wood-species identification based on terahertz spectral data augmentation and pseudo-label guided deep clustering","year":2023,"lang":"en","type":"article","venue":"Wood Material Science and Engineering","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Autoencoder; Cluster analysis; Discriminative model; Artificial intelligence; Pattern recognition (psychology); Computer science; Deep learning; Identification (biology); Machine learning; Mathematics; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000494723,0.0006076808,0.0003880069,0.0006965429,0.0002983712,0.0004970533,0.0009255718,0.0006040101,0.0007916718],"category_scores_gemma":[0.0007582328,0.0003233508,0.0006999726,0.0004902713,0.0005622417,0.001206228,0.0007994815,0.0007899605,0.0003504672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004849856,"about_ca_system_score_gemma":0.00056345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003050886,"about_ca_topic_score_gemma":0.006488322,"domain_scores_codex":[0.9997839,0.00003361848,0.000007565568,0.00008733052,0.00005446751,0.00003321005],"domain_scores_gemma":[0.9997297,0.00007435681,0.00003801579,0.00005820703,0.00008254775,0.00001712681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002623597,0.0001960207,0.00547758,0.000114962,0.00008800942,0.0001077056,0.0002227019,0.5678861,0.1283816,0.0121018,0.001545413,0.2836159],"study_design_scores_gemma":[0.000001969568,0.00001189047,0.0005172527,0.000002621275,0.000004320178,0.00001770011,0.00001151674,0.988624,0.008808617,0.001648408,0.000344594,0.000007117867],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08125948,0.0001003015,0.9167321,0.00006925302,0.00002031311,0.00002771356,0.00009556726,0.0005728018,0.001122435],"genre_scores_gemma":[0.644801,0.0001238267,0.3506364,0.0001117977,0.00001515425,0.00007879919,0.00058667,0.0001211199,0.003525286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003050886,"threshold_uncertainty_score":0.006066263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04481481709318126,"score_gpt":0.2903728878742032,"score_spread":0.245558070781022,"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."}}