{"id":"W4404641473","doi":"10.1016/j.rse.2024.114512","title":"Seasonal vegetation dynamics for phenotyping using multispectral drone imagery: Genetic differentiation, climate adaptation, and hybridization in a common-garden trial of interior spruce (Picea engelmannii × glauca)","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Natural Resources and Forestry; Government of British Columbia; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Remote sensing; Vegetation (pathology); Adaptation (eye); Picea engelmannii; Drone; Environmental science; Geography; Ecology; Physical geography; Biology; Botany","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003732359,0.0002219794,0.0003354374,0.00009633809,0.00008367521,0.00004552197,0.00007449689,0.00009839072,0.000008521937],"category_scores_gemma":[0.00005669599,0.0002204944,0.00008953515,0.0001508091,0.0001771763,0.0001565912,0.0001004107,0.0001317958,0.000003280827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005771397,"about_ca_system_score_gemma":0.00001557873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002383467,"about_ca_topic_score_gemma":0.0003962071,"domain_scores_codex":[0.9981598,0.0001512626,0.0006245647,0.0004401891,0.000354161,0.0002700093],"domain_scores_gemma":[0.9992959,0.000153909,0.000284318,0.0001944992,0.00001423874,0.00005712917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001814997,0.0001093909,0.001690027,0.0005191058,0.00009794516,0.00001811965,0.004010687,0.1672564,0.4311111,0.00004221697,0.00001626031,0.3933138],"study_design_scores_gemma":[0.002597427,0.0001031615,0.03993487,0.0004119371,0.0001001576,0.00002026786,0.0001426507,0.9520479,0.0040655,0.000347685,0.00002387097,0.00020459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.810863,0.0001466003,0.1879201,0.00009044386,0.0002453493,0.0006638177,0.00001144048,0.00002121735,0.0000381125],"genre_scores_gemma":[0.8899016,0.0001285385,0.1097882,0.000009091695,0.00006511044,2.741557e-7,0.00005630851,0.00003274882,0.00001809365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7847915,"threshold_uncertainty_score":0.8991503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142062090619115,"score_gpt":0.2300508838244813,"score_spread":0.2186302629182902,"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."}}