{"id":"W4306697739","doi":"10.1139/as-2021-0061","title":"Using drone mapping to evaluate error of plot-based field surveys and its effects on moderate spatial resolution remote sensing retrieval of lichen cover","year":2022,"lang":"en","type":"article","venue":"Arctic Science","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Environment and Climate Change Canada","funders":"Natural Resources Canada; Environment and Climate Change Canada","keywords":"Sampling (signal processing); Remote sensing; Plot (graphics); Field (mathematics); Land cover; Drone; Sample (material); Pixel; Sample size determination; Image resolution; Computer science; Cover (algebra); Environmental science; Statistics; Mathematics; Artificial intelligence; Geography; Computer vision; Ecology; Land use; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001867346,0.0003561475,0.0003255671,0.0003746359,0.0002587748,0.0004881346,0.0004333208,0.0004456106,0.0005258514],"category_scores_gemma":[0.0085489,0.0002149068,0.0003357665,0.000543708,0.0003025673,0.0006097973,0.0004119311,0.0002477012,0.00009686532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008135724,"about_ca_system_score_gemma":0.0003828043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02204134,"about_ca_topic_score_gemma":0.02797002,"domain_scores_codex":[0.9990646,0.0003637933,0.00006979025,0.0002001777,0.0002371854,0.00006439417],"domain_scores_gemma":[0.994473,0.003708119,0.0004217008,0.0007489756,0.0005964013,0.00005172544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001945575,0.0008030577,0.2473924,0.0003290551,0.0004287983,0.0001812446,0.0008634355,0.5408377,0.105449,0.000905748,0.000336324,0.1005277],"study_design_scores_gemma":[0.0000994853,0.001123431,0.2008687,0.00002994013,0.00009479691,0.0001935499,0.0002520154,0.7337757,0.06200579,0.0003726473,0.001107194,0.00007681458],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9711198,0.00006159423,0.027889,0.00001721428,0.000006459221,0.00005667641,0.000180127,0.0001489414,0.0005201314],"genre_scores_gemma":[0.9784271,0.00002776114,0.02106043,0.00001418855,0.000001375663,0.00004404613,0.0002142251,0.00002673014,0.0001841559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02204134,"threshold_uncertainty_score":0.0438261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04734883361747846,"score_gpt":0.2932444566239069,"score_spread":0.2458956230064284,"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."}}