New Experimental Methods For modelling Canopy Problems
Bibliographic record
Abstract
The challenge in large scale experimental fluid dynamics comes from the demand to develop simple methods that can measure and model movement of flexible objects and wind flow over large three-dimensional volumes with readily available equipment. Representing and modelling the flow around trees is challenging mainly because of the complexity and variability of trees. In this research different methods were tested in the novel Wind Engineering Energy and Environment (WindEEE) Dome facility at Western University, Canada to capture displacements of a moving single tree and flow velocities over a model forest edge in a three-dimensional form. The tested measuring methods use commonly available optical equipment and require little or no calibration prior to the experiments. The correlation between the wind force exerted on a single garden tree canopy and the resulted projected area as well as between the wind force and the crown displacement were determined using an infrared time-of-flight camera. Two spatial components of wind flow velocity over a modelled forest were measured using a digital camera, light projectors and tracer particles. A three-dimensional - two components colored flow visualization technique is also investigated. The calculated horizontal and vertical flow velocity components were compared with data measured with Cobra probes. The flow is compared with Computational Fluid Dynamics (CFD) simulations and visualized in a three-dimensional form. The techniques prove to have a good accuracy, are easy to implement, are quantitative methods and come as an alternative to using complex laser-based measurement techniques.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".