{"id":"W4307658112","doi":"10.1038/s41598-022-22446-z","title":"Optimal settings and advantages of drones as a tool for canopy arthropod collection","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Molson Foundation","keywords":"Drone; Canopy; Abundance (ecology); Arthropod; Predation; Ecology; Species richness; Biology; Sampling (signal processing); Wetland; Habitat; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001245312,0.0005031924,0.0003676969,0.0007293699,0.0004214552,0.0007815646,0.0004556999,0.0003742297,0.001403833],"category_scores_gemma":[0.004403822,0.0002839984,0.0002555701,0.0003237471,0.0002968473,0.001063969,0.0007437203,0.0002969339,0.0003573157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002228645,"about_ca_system_score_gemma":0.0003266929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001075291,"about_ca_topic_score_gemma":0.005264005,"domain_scores_codex":[0.9985222,0.0007331827,0.0001520916,0.0002264447,0.0002533923,0.0001126198],"domain_scores_gemma":[0.9966748,0.001644512,0.0006167445,0.0003274984,0.0004288535,0.0003076609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005495023,0.0016207,0.5012429,0.001329181,0.0003322332,0.0006876328,0.001673917,0.004825501,0.2183488,0.0006153223,0.000795934,0.2630329],"study_design_scores_gemma":[0.0002284544,0.008697221,0.8822623,0.0004985649,0.0005696207,0.002361086,0.004229982,0.009647142,0.08024341,0.0008944873,0.01017551,0.0001921459],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989734,0.001156401,0.006160792,0.00009856599,0.00001928825,0.000141344,0.0002555269,0.00005264197,0.002381509],"genre_scores_gemma":[0.9761015,0.0005627659,0.02260712,0.00004914806,0.00001492213,0.0001144839,0.0001740666,0.00001713148,0.0003590285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001403833,"threshold_uncertainty_score":0.006585896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003661069123267186,"score_gpt":0.219696277101862,"score_spread":0.2160352079785948,"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."}}