{"id":"W1971439468","doi":"10.1111/j.1654-109x.2007.tb00440.x","title":"Characterization of diverse plant communities in Aspen Parkland rangeland using LiDAR data","year":2007,"lang":"en","type":"article","venue":"Applied Vegetation Science","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Lidar; Shrubland; Vegetation (pathology); Understory; Shrub; Deciduous; Rangeland; Environmental science; Remote sensing; Canopy; Plant community; Grassland; Vegetation classification; Geography; Ecology; Physical geography; Agroforestry; Ecological succession; Ecosystem","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":[0.0003198354,0.0001118503,0.0001236817,0.0009972726,0.0003313592,0.0004353269,0.0002499772,0.00009160731,0.0003931421],"category_scores_gemma":[0.000563839,0.00006948426,0.00005994484,0.0006578599,0.000187344,0.0002394507,0.0002233804,0.00008408765,0.00005573356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008684327,"about_ca_system_score_gemma":0.000565839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2362199,"about_ca_topic_score_gemma":0.5827411,"domain_scores_codex":[0.9998595,0.00002288616,0.000007559302,0.00003050111,0.0000492098,0.00003040318],"domain_scores_gemma":[0.999655,0.00006216719,0.0000560283,0.0000164503,0.0001523322,0.00005809798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005076099,0.00002445167,0.9775839,0.0000196593,0.00001493365,0.00006407238,0.0004161083,0.0005096045,0.003180058,0.00006193831,0.00009117359,0.01798318],"study_design_scores_gemma":[0.000003514348,0.00001876913,0.9959059,0.000007134273,0.000007593272,0.00006461622,0.0009317364,0.002101277,0.0006000143,0.00003233969,0.0003241459,0.000002909942],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999427,0.00004823845,0.000176918,0.000005184916,2.392531e-7,0.000003977696,0.00008573214,0.000003618729,0.0002491701],"genre_scores_gemma":[0.99875,0.00003212159,0.0007995269,0.000004159474,5.21538e-7,0.000003747907,0.0002800254,7.569367e-7,0.0001291855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2362199,"threshold_uncertainty_score":0.4696898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05462106601487948,"score_gpt":0.287189371927523,"score_spread":0.2325683059126435,"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."}}