{"id":"W4412042670","doi":"10.1111/nph.70357","title":"Seeing herbaria in a new light: leaf reflectance spectroscopy unlocks trait and classification modeling in plant biodiversity collections","year":2025,"lang":"en","type":"article","venue":"New Phytologist","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Division of Environmental Biology; Division of Biological Infrastructure; Harvard University; National Science Foundation","keywords":"Herbarium; Trait; Linear discriminant analysis; Biodiversity; Digitization; Biology; Taxon; Botany; Ecology; Artificial intelligence; Computer science","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":[0.002545007,0.0003886351,0.0003816498,0.0005691433,0.0003485986,0.001074018,0.0006539455,0.0003444129,0.0005330604],"category_scores_gemma":[0.00363935,0.0002711761,0.0006100513,0.0006098421,0.0004257151,0.001142624,0.0009626294,0.0007282996,0.000311545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007313619,"about_ca_system_score_gemma":0.0003756611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006358312,"about_ca_topic_score_gemma":0.01238856,"domain_scores_codex":[0.9992027,0.0003423762,0.00002488934,0.0002382349,0.0001353438,0.00005653873],"domain_scores_gemma":[0.9980999,0.0008893997,0.000336135,0.0003654173,0.0001917129,0.0001175738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007095253,0.0005758446,0.4726745,0.0001779547,0.0005568677,0.0002047125,0.0007134418,0.218444,0.1414825,0.001682963,0.001999697,0.1607781],"study_design_scores_gemma":[0.00001402596,0.0001442599,0.2392678,0.00002140545,0.00006790373,0.00007672497,0.0002732454,0.7391366,0.01772077,0.001967813,0.001254544,0.00005483177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546403,0.00006923317,0.04352948,0.0001431729,0.000008409478,0.00002516239,0.000328045,0.0003683654,0.0008877339],"genre_scores_gemma":[0.9658846,0.00004957052,0.03310192,0.00005005803,0.000009665473,0.000024306,0.0005575105,0.00006170409,0.0002606302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006358312,"threshold_uncertainty_score":0.0134595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02672535149413607,"score_gpt":0.2483010741689571,"score_spread":0.221575722674821,"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."}}