A Tethered And Navigated Air Blimp (TANAB) for observing themicroclimate over a complex terrain
Bibliographic record
Abstract
Abstract. This study presents the first environmental monitoring field campaign of a newly developed Tethered And Navigated Air Blimp (TANAB) system to investigate the microclimate over a complex terrain. The use of a tethered balloon in complex terrains such as mines and tailings ponds is novel and the focus of the present study. The TANAB system was fully developed and launched at a mine facility in northern Canada in May 2018. This study describes the key design features, the sensor payload onboard, and the observations made by the TANAB system. The system measured meteorological conditions including wind speed in three directions, temperature, relative humidity, and pressure over the first few tens of meters of the atmospheric boundary layer. The system also performed earth surface thermal imaging, or temperature mapping, of the underlying surface. The measurements were made at two primary locations in the facility: i) near a tailings pond and ii) in a mine pit. TANAB measured the dynamics of the atmosphere at different diurnal times (e.g. day versus night) and locations (near tailings pond versus inside the mine). Such dynamics include mean and turbulence statistics pertaining to flow momentum and energy, and they are crucial in the understanding of emission fluxes from the facility in future studies. In addition, TANAB can provide boundary conditions and validation datasets to support mesoscale dispersion modelling or Computational Fluid Dynamics (CFD) simulations for various transport models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".