{"id":"W4236510503","doi":"10.5194/isprsarchives-xli-b1-1017-2016","title":"DESIGN AND IMPLEMENTATION OF A LOW-COST UAV-BASED MULTI-SENSOR PAYLOAD FOR RAPID-RESPONSE MAPPING APPLICATIONS","year":2016,"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":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Norges Forskningsråd; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Payload (computing); Geospatial analysis; Computer science; Real-time computing; Response time; Embedded system; Drone; Architecture; Field (mathematics); Systems engineering; Engineering; Computer network; Remote sensing; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001331691,0.0003359411,0.0003514546,0.0008469173,0.0007498214,0.0003332911,0.0007852783,0.00007913382,0.000004831144],"category_scores_gemma":[0.0004142331,0.0002070311,0.0002378708,0.0006338743,0.001709676,0.0003384953,0.0002522593,0.0001731039,0.000001067332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000487115,"about_ca_system_score_gemma":0.0001767726,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1898126,"about_ca_topic_score_gemma":0.02838688,"domain_scores_codex":[0.9968051,0.0002537336,0.001279239,0.0002995311,0.0009676221,0.0003947807],"domain_scores_gemma":[0.9963595,0.001814719,0.001037473,0.0003424333,0.0003247519,0.0001211506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002067789,0.00001426582,0.0001447924,0.00007945916,0.00006805845,7.30749e-8,0.001541885,0.03003879,0.02018297,0.000005724992,0.00001808708,0.9476991],"study_design_scores_gemma":[0.001343828,0.000110212,0.002549124,0.0003184853,0.00003595457,0.00002574668,0.001388489,0.9558843,0.03527064,0.001124112,0.001701255,0.0002478825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008175623,0.00002605596,0.987314,0.001954464,0.0006431916,0.001383956,0.0001778734,0.00004875896,0.0002760449],"genre_scores_gemma":[0.9801808,0.000149477,0.01915359,0.0003800344,0.00006256442,0.000002271501,0.00003378827,0.00001452004,0.00002292679],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9720052,"threshold_uncertainty_score":0.9893425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341096068323929,"score_gpt":0.2690969686959956,"score_spread":0.2456860080127563,"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."}}