{"id":"W4413197164","doi":"10.1101/2025.08.08.669192","title":"Procrustean pseudo-landmark methods in Python to measure massive quantities of leaf shape data","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"National Institutes of Health; National Science Foundation","keywords":"Shape analysis (program analysis); Procrustes analysis; Landmark; Python (programming language); Phyllotaxis; Principal component analysis; Mathematics; Artificial intelligence; Pattern recognition (psychology); Computer science; Biology; Botany; Ecology","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":[0.003025382,0.00185886,0.001247947,0.001816159,0.0009106597,0.002314276,0.003330848,0.001029067,0.02051307],"category_scores_gemma":[0.01205021,0.00131446,0.002310416,0.00213955,0.001410925,0.002267642,0.004404715,0.003413355,0.01411863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005604083,"about_ca_system_score_gemma":0.002273762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002194079,"about_ca_topic_score_gemma":0.004015523,"domain_scores_codex":[0.9981403,0.0003607645,0.0001640171,0.0004502118,0.0007530859,0.000131659],"domain_scores_gemma":[0.9956743,0.001861129,0.0005016603,0.00117741,0.0005870821,0.0001983646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009929884,0.0003618134,0.01277382,0.002367565,0.0008311311,0.0006303369,0.00155955,0.05201336,0.07095555,0.03951537,0.3096576,0.508341],"study_design_scores_gemma":[0.0002747686,0.0002181587,0.01195713,0.000197714,0.00009800884,0.0008160942,0.0002615145,0.6582791,0.07297031,0.08329047,0.1712905,0.0003463278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003751903,0.00006652526,0.8496614,0.0001363342,0.00009152851,0.0001081688,0.00500616,0.1402559,0.0009221389],"genre_scores_gemma":[0.03763732,0.000152303,0.9109275,0.0003134756,0.00005780716,0.001499769,0.01346702,0.03370247,0.002242322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02051307,"threshold_uncertainty_score":0.06862313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08287782000095513,"score_gpt":0.2858751114655493,"score_spread":0.2029972914645942,"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."}}