{"id":"W2274694356","doi":"10.5194/isprsarchives-xl-1-w4-241-2015","title":"WILDLIFE MULTISPECIES REMOTE SENSING USING VISIBLE AND THERMAL INFRARED IMAGERY ACQUIRED FROM AN UNMANNED AERIAL VEHICLE (UAV)","year":2015,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Géomatique du Québec; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Wildlife; Multispectral image; Aerial survey; Remote sensing; Aerial imagery; Workflow; Environmental science; Computer science; Geography; Cartography; Ecology; Biology","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":[0.0002622911,0.0002653213,0.0001709572,0.001058191,0.0001539287,0.0003033089,0.0001632451,0.0001322609,0.0008266955],"category_scores_gemma":[0.0002024946,0.0001266816,0.0001864077,0.0004263865,0.00009107799,0.000343157,0.0002639614,0.0001096967,0.0002302587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001075014,"about_ca_system_score_gemma":0.0001008324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001351319,"about_ca_topic_score_gemma":0.003938332,"domain_scores_codex":[0.9998301,0.00004054499,0.000008026977,0.00004874933,0.00005677052,0.00001582164],"domain_scores_gemma":[0.9997702,0.00004068758,0.00006215178,0.00002655694,0.0000760783,0.00002440669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003962537,0.0002031916,0.15725,0.0003319456,0.0002566743,0.0003219568,0.0003590037,0.007020265,0.4699701,0.0004181848,0.001567005,0.3619054],"study_design_scores_gemma":[0.00002828212,0.0007914744,0.7480938,0.00009867409,0.0002594069,0.001530058,0.0009151149,0.1178641,0.1249192,0.0005000898,0.00492805,0.00007173506],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9469868,0.00042406,0.04918182,0.00003500721,0.00002950757,0.00006032999,0.0005392825,0.0003731466,0.002370106],"genre_scores_gemma":[0.9293056,0.0001072538,0.06963773,0.00002633665,0.000007086736,0.00001903097,0.0002895453,0.00001293341,0.0005945969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001351319,"threshold_uncertainty_score":0.002765536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02415272128928667,"score_gpt":0.2581875427463083,"score_spread":0.2340348214570217,"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."}}