Numerical modelling of airflow within and above forests and forest clearings using computational fluid dynamics.
Bibliographic record
Abstract
The computational fluid dynamics program, FLUENT, was first tested to validate windtunnel measurements of a scaled 10 ha forest clearing in a two dimensional domain. A variety of domain and canopy configurations were examined along with processor settings. Validation of the CFD program produced excellent results for horizontal wind velocity. Conifer shaped tree elements for the forest stands performed well and similar to the more traditional way of representing forest canopies. Turbulent kinetic energy (TKE) values output by the program seem to over predict the values calculated by using wind tunnel statistics. Various sizes of forest clearings were simulated to determine the stress that would be experienced by a forest edge immediately downwind of a clearing. Shorter gaps (<15 tree heights) seem to experience high values of TKE over the downwind forest, compared to the stand upwind of the clearing and lower stress values along the downwind forest edge. Large gaps (>60 tree heights) saw higher stress values but TKE values no larger than those reported upwind of the clearing. From the stress values calculated from various input velocities and gap sizes, a new tool was produced which takes into account a sites endemic wind speed and canopy density to predict stress on forest edges downwind of clearings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".