{"id":"W4407930119","doi":"10.1007/s00226-025-01636-8","title":"Unsupervised wood species identification based on multiobjective optimal clustering and feature fusion","year":2025,"lang":"en","type":"article","venue":"Wood Science and Technology","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Identification (biology); Cluster analysis; Feature (linguistics); Fusion; Artificial intelligence; Pattern recognition (psychology); Computer science; Engineering; Mathematics; Biology; Botany","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":[0.0008620963,0.0009083083,0.001314998,0.00232257,0.0007426441,0.0009601635,0.000949265,0.000913304,0.000905657],"category_scores_gemma":[0.001294204,0.0004687832,0.001599544,0.001635806,0.0004854299,0.001157356,0.0009576196,0.0005599342,0.0003868522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004915457,"about_ca_system_score_gemma":0.0008235709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003048996,"about_ca_topic_score_gemma":0.003821819,"domain_scores_codex":[0.9994228,0.00009779744,0.00003739494,0.0001728383,0.0001820657,0.00008715689],"domain_scores_gemma":[0.9993962,0.0001885084,0.00008484499,0.00007110215,0.000224349,0.00003501525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003761523,0.000360855,0.004510127,0.0002049266,0.0002860507,0.0001274762,0.0002557714,0.3776107,0.09244861,0.004098927,0.001725445,0.5179949],"study_design_scores_gemma":[0.000005734687,0.00003839892,0.001742628,0.000005803249,0.00002601955,0.00003115861,0.00003713798,0.9901536,0.005539906,0.002130597,0.0002704615,0.00001854067],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05565107,0.0001527765,0.9425008,0.00005020571,0.00002183643,0.00006106769,0.0001136563,0.000430816,0.001017689],"genre_scores_gemma":[0.5222579,0.0001111124,0.4754395,0.00003991483,0.00002845723,0.0001317965,0.0005272957,0.0001090565,0.00135489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003048996,"threshold_uncertainty_score":0.006062508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009943913753053705,"score_gpt":0.2644120739454686,"score_spread":0.2544681601924149,"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."}}