{"id":"W4413165931","doi":"10.1101/2025.08.11.669697","title":"Predicting flowering time using integrated morphophysiological and genomic data with machine learning models","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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Machine learning; Biology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009993598,0.0005333173,0.0005008954,0.001406524,0.0002151953,0.0005969748,0.0004490009,0.0003739277,0.000554823],"category_scores_gemma":[0.001464914,0.0001746451,0.0008362636,0.0007909118,0.0001947268,0.0003257192,0.000351596,0.0004562257,0.0002300395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005482895,"about_ca_system_score_gemma":0.0004375758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01044372,"about_ca_topic_score_gemma":0.01078967,"domain_scores_codex":[0.9997236,0.00009356783,0.00001639948,0.0001051313,0.00002986213,0.0000313619],"domain_scores_gemma":[0.999034,0.0006191331,0.0001353576,0.00007910103,0.00009269084,0.00003964502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006557874,0.0005266626,0.3904265,0.0001176408,0.0007448235,0.0001968575,0.0000804259,0.4110509,0.03007507,0.0006928867,0.001166957,0.1642654],"study_design_scores_gemma":[0.000005843869,0.00003324763,0.02872135,0.000005187518,0.00003134303,0.00002139907,0.00001690523,0.9694201,0.001097829,0.000494652,0.0001442211,0.00000799763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8590677,0.0004237636,0.1369608,0.0001747469,0.00001946898,0.00003720421,0.001715202,0.0008835687,0.0007175365],"genre_scores_gemma":[0.9784549,0.00003892744,0.02004079,0.00002242923,0.00001292162,0.00001984555,0.001165705,0.00001325301,0.0002311394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01044372,"threshold_uncertainty_score":0.02076584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04794798826285897,"score_gpt":0.2034029237601817,"score_spread":0.1554549354973227,"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."}}