Downslope Flows on a Low-Angle Slope and Their Interactions with Valley Inversions. Part I: Observations
Bibliographic record
Abstract
Abstract Thermally driven downslope flows were investigated on a low-angle (1.6°) slope on the west side of the floor of Utah’s Salt Lake Valley below the Oquirrh Mountains using data from a line of four tethered balloons running down the topographic gradient and separated by about 1 km. The study focused on the evolution of the temperature and wind structure within and above the slope flow layer and its variation with downslope distance. In a typical situation, on clear, undisturbed October nights a 25-m-deep temperature deficit of 7°C and a 100–150-m-deep downslope flow with a jet maximum speed of 5–6 m s−1 at 10–15 m AGL developed over the slope during the first 2 h following sunset. The jet maximum speed and the downslope volume flux increased with downslope distance. The downslope flows weakened in the late evening as the stronger down-valley flows expanded to take up more of the valley atmosphere and as ambient stability increased in the lower valley with the buildup of a nocturnal temperature inversion. Downslope flows over this low-angle slope were deeper and stronger than has been reported previously by other investigators, who generally investigated steeper slopes and, in many cases, slopes on the sidewalls of isolated mountains where the downslope flows are not subject to the influence of nighttime buildup of ambient stability within valleys.
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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 | 0.001 |
| 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) | 0.001 | 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 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".