{"id":"W4409595554","doi":"10.1101/2025.04.13.648654","title":"Assessing plant phenological changes based on drivers of spring phenology","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Ontario Forest Research Institute","funders":"Lakehead University; Ontario Ministry of Natural Resources and Forestry; Guangxi Normal University; Ministry of Natural Resources","keywords":"Phenology; Spring (device); Climatology; Environmental science; Ecology; Biology; Engineering; Geology","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":[0.001094479,0.0004271425,0.0003316983,0.001186177,0.0002510766,0.0005532575,0.0002702799,0.0002512939,0.001062569],"category_scores_gemma":[0.002057783,0.0001200126,0.0006259358,0.001251037,0.0002207547,0.000489406,0.0004822123,0.0004003942,0.0003070412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00031094,"about_ca_system_score_gemma":0.0002687136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004547441,"about_ca_topic_score_gemma":0.01103986,"domain_scores_codex":[0.9995889,0.0000959628,0.0000268649,0.0002130234,0.00003946283,0.00003592053],"domain_scores_gemma":[0.9987526,0.00057118,0.000307355,0.0001683497,0.00009666734,0.0001037571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001684629,0.0000349032,0.9611962,0.0003253604,0.0004836381,0.00006353239,0.0002229628,0.005941244,0.01387242,0.0002325537,0.001146397,0.01631231],"study_design_scores_gemma":[0.000006763939,0.00003573963,0.9882345,0.00001053306,0.00006525553,0.00006268464,0.0001178326,0.007207692,0.0008346641,0.0003109957,0.003105111,0.000008264218],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973079,0.001038263,0.006846552,0.0001181638,0.00002758019,0.00002444213,0.01743891,0.0002185159,0.001208623],"genre_scores_gemma":[0.9747079,0.000250111,0.005264361,0.00005675885,0.00002650843,0.00004293741,0.01932514,0.00004186952,0.0002844882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004547441,"threshold_uncertainty_score":0.009041965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01727225227309416,"score_gpt":0.2210007722535538,"score_spread":0.2037285199804596,"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."}}