{"id":"W3022209555","doi":"10.5194/gi-9-193-2020","title":"A Tethered Air Blimp (TAB) for observing the microclimate over a complex terrain","year":2020,"lang":"en","type":"article","venue":"Geoscientific instrumentation, methods and data systems","topic":"Aerospace Engineering and Energy Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung Advanced Institute of Technology; University of Alberta; University of Guelph","keywords":"Terrain; Environmental science; Mesoscale meteorology; Microclimate; Meteorology; Wind speed; Wind tunnel; Payload (computing); Wind direction; Planetary boundary layer; Tailings; Boundary layer; Turbulence; Calibration; Aerospace engineering; Engineering; Geography; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0001931975,0.0003486653,0.0002664964,0.0005844989,0.0002918441,0.0003456375,0.0004432645,0.000387336,0.001959391],"category_scores_gemma":[0.0002704054,0.0001625923,0.0002405034,0.0004923759,0.0001220003,0.0004640022,0.0005434842,0.0003804214,0.0006131025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002143326,"about_ca_system_score_gemma":0.0003060484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002042415,"about_ca_topic_score_gemma":0.00693,"domain_scores_codex":[0.9998007,0.00002013605,0.000004659792,0.00005378993,0.00009441093,0.00002627646],"domain_scores_gemma":[0.9998067,0.00002736939,0.00003035198,0.00003323644,0.0000682348,0.00003406358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006129971,0.0002670396,0.1012468,0.0004434823,0.000169265,0.000441396,0.0005173441,0.006559846,0.6223534,0.0005084557,0.006579598,0.2603004],"study_design_scores_gemma":[0.0002393996,0.002522618,0.577843,0.0001344495,0.0003060665,0.001954445,0.0009993952,0.1524045,0.2081134,0.0009152604,0.05439715,0.0001703465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.881968,0.0006128711,0.0959463,0.0002367437,0.0002301972,0.0002988349,0.004945266,0.003581799,0.01218],"genre_scores_gemma":[0.9120409,0.0001979974,0.08229915,0.0001895277,0.00003483218,0.0001242884,0.00202839,0.0001070559,0.002977807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002042415,"threshold_uncertainty_score":0.006554782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0899892037677107,"score_gpt":0.3392601915894423,"score_spread":0.2492709878217316,"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."}}