Bibliographic record
Abstract
Gap, outflow, or Squamish wind, is the cold low level seaward flow of air through fjords which dissect the coastal mountain barrier of northwestern North America. These flows, occurring mainly during winter, can be strong, threatening safety, economic activity and comfort. Howe Sound gap winds were studied using a combination of observations and several types of models. Observations of winds in Howe Sound showed that gap wind strength varied considerably along the channel, across the channel and vertically. Generally, winds increase down the channel, are strongest along the eastern side, and are below 1000 m depth. Observations were unable to answer all questions about gap winds due to data sparseness, particularly in the vertical direction. Therefore, several modelling approaches were used. The modelling began with a complete 3-dimensional quasi-Boussinesq model (CSURAMS) and ended with the creation and testing of models which are conceptually simpler, and more easily interpreted and manipulated. A gap wind simulation made using RAMS was shown to be mostly successful by statistical evaluation compared to other mesoscale simulations, and by visual inspection of the fields. The RAMS output, which has very high temporal and spatial resolution, provided much additional information about the details of gap flow. In particular, RAMS results suggested a close analogy between gap wind and hydraulic channel flow, with hydraulic features such as supercritical flow and hydraulic jumps apparent. These findings imply gap wind flow could potentially be represented by much simpler models. The simplest possible models containing pressure gradient, advection and friction but not incorporating hydraulic effects, were created, tested, and found lacking. A hydraulic model, which in addition incorporates varying gap wind height and channel geometry, was created and shown to successfully simulate gap winds. Force balance analysis from RAMS and the hydraulic model showed that pressure gradient and advection are the most important forces, followed by friction which becomes an important force in fast supercritical flow. The sensitivity of gap wind speed to various parameters was found from sensitivity tests using the hydraulic model. Results indicated that gap wind speed increases with increasing boundary layer height and speed at the head of channel, and increasing synoptic pressure gradient. Gap wind speed decreases with increasing friction, and increasing boundary layer height at the seaward channel end. Increasing temperature dilterences between the cold gap wind air and the warmer air aloft was found to increase the variability of the flow — higher maximum but lower mean wind speeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".