{"id":"W4409890435","doi":"10.37207/crm.5.1s","title":"The dimensional data of American beech trees in changing climates: environmental data science meets digital art installations in public space","year":2025,"lang":"en","type":"article","venue":"Climanosco Research Manuscripts","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Université du Québec à Montréal; Université TÉLUQ","funders":"","keywords":"Beech; Space (punctuation); Environmental science; Geography; Computer science; Forestry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001381177,0.0001725062,0.0001378143,0.002047979,0.00214321,0.005961538,0.0004388372,0.0005373801,0.009846174],"category_scores_gemma":[0.004424881,0.000190409,0.0002146787,0.005215707,0.001810496,0.003310056,0.00197942,0.001061231,0.001050576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002460691,"about_ca_system_score_gemma":0.001479154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06432991,"about_ca_topic_score_gemma":0.2382351,"domain_scores_codex":[0.9993383,0.0001733522,0.00002111132,0.00007977035,0.0003005224,0.00008682002],"domain_scores_gemma":[0.9975073,0.000904177,0.0001833007,0.0005013818,0.0006337412,0.000270216],"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":[0.0003088203,0.0001436932,0.1086731,0.0005885494,0.000060661,0.0009987437,0.0658622,0.006384889,0.004884445,0.0572025,0.2460068,0.5088857],"study_design_scores_gemma":[0.00001143663,0.00003728573,0.1875466,0.0003007002,0.0000329873,0.0002727481,0.05104855,0.004154705,0.001587185,0.01964505,0.7352708,0.00009202504],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5424949,0.005104499,0.02149954,0.02926225,0.001240055,0.0001899788,0.02562569,0.001721184,0.3728619],"genre_scores_gemma":[0.9487653,0.002238677,0.01873444,0.0007026811,0.0003050115,0.00006674206,0.005602286,0.0004776133,0.02310718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06432991,"threshold_uncertainty_score":0.127911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1269368725579249,"score_gpt":0.3757401309719025,"score_spread":0.2488032584139775,"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."}}