{"id":"W3034551044","doi":"10.3390/f11060682","title":"Quantification of Lichen Cover and Biomass Using Field Data, Airborne Laser Scanning and High Spatial Resolution Optical Data—A Case Study from a Canadian Boreal Pine Forest","year":2020,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Lichen; Canopy; Environmental science; Taiga; Biomass (ecology); Abundance (ecology); Remote sensing; Tree canopy; Vegetation (pathology); Ecology; Forestry; Physical geography; Geography; Biology","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":[0.0004708527,0.0004586321,0.0002420675,0.001293896,0.0009542393,0.0008835719,0.0005449168,0.000296671,0.0003860685],"category_scores_gemma":[0.0008791956,0.0001659378,0.0002586547,0.001695225,0.00038351,0.0003203102,0.0002700772,0.0001782824,0.00009643252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004833578,"about_ca_system_score_gemma":0.002449381,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9269823,"about_ca_topic_score_gemma":0.9750541,"domain_scores_codex":[0.9997043,0.00002558126,0.00001436846,0.00007244087,0.000116671,0.00006651905],"domain_scores_gemma":[0.9992369,0.0001707177,0.00009912564,0.00004867648,0.0003627041,0.00008180865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001083353,0.0001113182,0.9528465,0.00006171117,0.00006749466,0.0004581717,0.0007289437,0.00916581,0.007574063,0.0001153356,0.0002712264,0.02849113],"study_design_scores_gemma":[0.000005573768,0.00003036062,0.9795914,0.000008951093,0.00002148114,0.0001262349,0.0008798917,0.01762432,0.001102966,0.00003733918,0.0005549423,0.00001644146],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983208,0.0000987585,0.0005116226,0.00001183167,6.704893e-7,0.00001662446,0.0004485289,0.00002254478,0.0005685826],"genre_scores_gemma":[0.9964969,0.00008338032,0.002204608,0.000007157561,0.000001472485,0.000007517573,0.0008604297,0.00000493887,0.0003336797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07301766,"threshold_uncertainty_score":0.1468953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04679528416550158,"score_gpt":0.2821252913254337,"score_spread":0.2353300071599322,"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."}}