{"id":"W2991445281","doi":"10.1139/juvs-2019-0001","title":"Assessment of residual slash coverage using UAVs and implications for aspen regeneration","year":2019,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Slash (logging); Residual; Environmental science; Regeneration (biology); Forestry; Vegetation (pathology); Agroforestry; Geography; Biology; Mathematics; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0005642672,0.0002947339,0.0002134343,0.0006830238,0.0002348802,0.0005506326,0.0001847733,0.0001901919,0.0004276282],"category_scores_gemma":[0.001011882,0.00009765923,0.0001386298,0.0004703789,0.0002135029,0.0004342721,0.0002499936,0.0001721684,0.0001376256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002381447,"about_ca_system_score_gemma":0.00009893013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005906319,"about_ca_topic_score_gemma":0.01802511,"domain_scores_codex":[0.9997249,0.00004964217,0.00001853576,0.00005985899,0.000101329,0.00004570025],"domain_scores_gemma":[0.999267,0.000251665,0.0001901632,0.00005689927,0.0001747189,0.00005950619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005201058,0.00007756421,0.8186886,0.0001241097,0.0000890665,0.0002846286,0.0003154148,0.01001056,0.09832724,0.0001076679,0.0002039122,0.07125103],"study_design_scores_gemma":[0.000002809693,0.0002107335,0.9545974,0.00002287459,0.00002828649,0.0002220767,0.000581156,0.02981514,0.01405614,0.00009067436,0.0003601537,0.00001257417],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969285,0.0001459274,0.002112461,0.00001145835,0.000003802679,0.000007976312,0.0001431437,0.00003458077,0.0006121714],"genre_scores_gemma":[0.9979652,0.00004400262,0.001749546,0.000005150311,0.00000115746,0.000002523139,0.00009658468,0.000003551727,0.0001323181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005906319,"threshold_uncertainty_score":0.01174384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01992689296632519,"score_gpt":0.2914969366851549,"score_spread":0.2715700437188297,"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."}}